Quectel Wireless Solutions, a global IoT solutions provider, today announces a collaboration with Syrma SGS Technology Ltd, a leading electronic manufacturing services provider, to facilitate the manufacturing of modules in India, aligning with the Make in India initiative.

The collaboration is designed to leverage both Quectel’s extensive experience in cellular and connectivity modules and Syrma SGS’s innovative and efficient electronic system design and manufacturing skills to manufacture a range of IoT modules in India for customers both in India and globally.

The Make in India initiative was launched in September 2014 as part of a wider set of nation-building initiatives and is designed to transform India into a global design and manufacturing hub. With this partnership, Quectel is leveraging India’s ascent as a global leader in electronics manufacturing sector and the strategic choice to establish manufacturing operations in India enables Quectel to broaden its business footprint and explore new models within the country. As India’s technology industry continues to flourish, Quectel is well-equipped to cater to its evolving needs through this partnership.

“By combining Quectel’s cutting-edge technology and Syrma SGS’s expertise in electronics manufacturing, we aim to deliver unparalleled innovation to the market,” commented Norbert Muhrer, President and CSO, Quectel Wireless Solutions.

“This synergy reflects our shared commitment to providing top-notch, reliable solutions, and we are excited about the transformative impact this collaboration will have on the industry, enabling us to effectively meet the evolving demands of the Indian and global market.”

Mr. Krishna Pant Co-founder, Syrma SGS said “This collaboration signifies a union of Syrma SGS’s excellence in electronics manufacturing and Quectel’s cutting-edge technology, promising to deliver unparalleled value to our clients and the industry at large.”

The partnership is poised to bring about a positive impact on the electronic manufacturing ecosystem delivering high-quality products and services to clients worldwide, offering customers a broader spectrum of solutions and empowering businesses to thrive in the era of connected devices and smart technologies. The collaboration aims to cater to the diverse needs of industries, including telecommunications, automotive, smart metering, sound boxes, and other sectors. The partnership will provide clients with solutions that meet their specific requirements, and both companies share a commitment to upholding the highest standards of quality in their products and services.

Quectel’s global presence will play a pivotal role in establishing a seamless and efficient supply chain. This international reach ensures timely delivery and comprehensive support across borders, reinforcing the partnership’s ability to meet the demands of a wide array of industries.

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The Global AI Safety Summit 2023, held at Bletchley Park and chaired by the UK, was a ground-breaking event that brought together 150 global leaders from various sectors to discuss the future of Artificial Intelligence (AI).

The agreement on the Bletchley Declaration marked the Summit, emphasising collaborative action for AI safety and the need for a shared understanding of AI risks and opportunities. A significant development was the initiation of the State of the Science Report, led by Turing Award-winning scientist Yoshua Bengio, aimed at providing a science-based perspective on the risks and capabilities of frontier AI.

During the Summit, there was a strong focus on the necessity of state-led testing of AI models, and the importance of setting international safety standards was highlighted. The UK’s announcement of launching the world’s first AI Safety Institute underlined its commitment to leading in AI safety research and testing. Summit participants also recognised the need to address current and future AI risks, emphasising standardisation and interoperability to mitigate these risks effectively.

While the majority of current conversations surrounding the impact of AI remain broad and high-level, it’s crucial to acknowledge the significant influence it will have in the realm of IoT. As we delve into this specific area, it is evident that not only will AI play a pivotal role in shaping IoT’s evolution, but the reverse is also true.

The data generated from IoT applications will not only feed into AI systems, enhancing their capabilities, but also emphasise the importance of data integrity. This mutual influence underscores a dynamic relationship where both IoT and AI will significantly shape each other’s development, making it imperative to recognise and address the intertwined futures of these technologies.

In fact, the evolution of AI’s capabilities in processing the vast data generated by IoT devices is propelling a transition from reactive to proactive and predictive operations across various sectors. This paradigm shift is not only about efficiency and reliability but also about establishing trusted and authentic data sources, which is where the Identity of Things (IDoT) comes into play.

Moving from basic identifiers to unique digital identities, IDoT ensures the authenticity of data and strengthens the trust in IoT ecosystems. Implementing technologies such as embedded SIM (eSIM) and integrated SIM (iSIM) is instrumental in this process. They enable better security through robust access control, enhanced data integrity, and reduced vulnerabilities while also addressing privacy concerns.

By ensuring compliance with regulatory standards, eSIM and iSIM contribute to standardisation and reliability, which are critical for scalable and interoperable IoT networks. These technologies support personalisation and accountability, leading to enhanced traceability and the capacity for more advanced predictive analytics.

As AI and IoT continue to converge, the focus on unique digital identities through IDoT will become a cornerstone in achieving a secure, reliable, and adaptable technological ecosystem, ready for the future of interconnected devices.

However, a critical aspect of integrating AI with IoT is ensuring the data integrity of the inputs. The data sourced for AI processing must be not only authentic and secure but also trustworthy. This is because the decisions made by AI are only as reliable as the data upon which they are based. Any security vulnerabilities at the point of data collection or transmission could lead to significant, potentially catastrophic, consequences.

It is, therefore, essential for multi-party IoT ecosystems to establish and maintain data integrity to prevent such risks. Technologies such as SIGNiT by G+D are addressing this critical need by employing digital signing of data generated by IoT devices, coupled with blockchain technology, to create a secure and trustworthy data environment. Ensuring the fidelity of data at its source is fundamental to building AI systems that can be trusted to make sound decisions.

The path forward is fraught with challenges, particularly concerning data privacy, AI’s decision-making transparency, and the reliability of AI algorithms. A significant concern is ensuring that AI integration does not inadvertently create vulnerabilities within IoT systems. To significantly mitigate these risks, we can harness advanced cryptographic techniques.

For instance, elliptic curve cryptography (ECC) is one such technique that provides high levels of security with smaller key sizes, making it more efficient for IoT devices which often have limited computational power. By incorporating blockchain technology and employing advanced cryptography like ECC, we can establish robust security protocols to protect data integrity and maintain the trustworthiness of AI-driven IoT systems.

In essence, the integrity of the entire data stream can be maintained by securing data right at the source – the IoT sensor – and using private keys on secure elements like SIM cards. However, integrating AI into existing IoT systems presents issues beyond data integrity alone. Such integration is a complex endeavour that demands a multifaceted and sophisticated approach to tackle various technical and operational challenges.

On the technical front, it involves ensuring compatibility between AI algorithms and diverse IoT devices, managing the vast data streams generated by these devices, and maintaining the responsiveness and reliability of the systems. The integration must be seamless, ensuring that AI algorithms can effectively interpret and act on the data from IoT devices without causing system lags or errors.

Moreover, this integration significantly impacts business models and operational workflows. For businesses, incorporating AI into IoT systems often means rethinking how they collect, analyse, and utilise data for decision-making. It requires shifting from traditional business processes to a more dynamic, data-driven approach.

Operationally, there’s a need for continuous monitoring and maintenance of these integrated systems, ensuring they operate efficiently and effectively. This shift also necessitates training and upskilling of staff to manage and leverage these advanced systems.

The overarching goal is to ensure that AI acts as a catalyst for enhancing IoT functionalities, not as a barrier. It should streamline operations, provide deeper insights, and open new avenues for innovation and efficiency rather than complicate or hinder existing processes. Thus, integrating AI into IoT systems is not just a technological upgrade but a transformative process that reshapes how organisations operate and interact with technology.

The successful implementation of this integration hinges on a careful balance – leveraging the advanced capabilities of AI to enhance IoT functionalities while also adapting to the new challenges and opportunities this fusion presents, with a clear and necessary focus on data integrity.

As we stand at the cusp of a technological revolution with AI and IoT at its core, balancing the immense opportunities with the inherent challenges is imperative. Ensuring data integrity, securing IoT ecosystems, and maintaining a controlled integration of AI are essential steps towards harnessing the full potential of these technologies.

The AI Safety Summit is just the beginning of a critical journey. The real challenge lies ahead in our industry’s hands. In the IoT sector, we must actively drive the development of responsible and effective strategies for AI integration. While the Summit set the stage, it’s now our responsibility to act.

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Neue announces its sensor as a service offering that integrates world-leading security out-of-the-box from Kigen to dramatically reduce key product development hurdles for companies seeking cutting-edge connectivity solutions, enabling the development of groundbreaking connected products.

The combination of the solutions that address two principal challenges faced by product designers looking to industrialize always-on, connected products, the no-code, quick path offers significant speed-to-market.

Empowering Connected Products:

Neue, celebrated for its innovative IoT platform, has teamed up with Kigen, a leading expert in securing connected solutions. The collaboration empowers clients globally using the Neue iEnbl, ensuring that wherever there is a need for a SIM, eSIM, or iSIM, these are readily available from the very start in a way that extends to full-scale deployments.

This partnership sets the stage for developing a wide array of new connected products and services, from smart devices to industrial IoT solutions and beyond. The Neue iEnbl has been adopted by 30 customers at various stages, from prototyping to large scale deployments, and is now generally available. The versatile suite of technologies packed in the Neue iEnbl hardware, iSIM enablement, and no-code software reduces on average, between 6-12 months of development time. Customers can leverage manufacturing expertise from Neue’s and Kigen’s mutual partner, Flex.

The collaboration combines the strengths offering:

1. Seamless Connectivity: Effortlessly integrate SIM, eSIM, and iSIM technologies into products and services using the Neue iEnbl that support a number of connectivity options: LTE Cat-M, NB-IoT, 5G, WiFi, BLE 4.2, RS-232/RS-485, GNSS (GPS, GLONASS, GALILEO, BeiDou).

2. Security: Leverage Kigen’s secure OS for all types of eSIM and iSIM, with expertise in secure connectivity to guarantee data protection and reliability in an increasingly connected world.

A Unified Vision:

“We are thrilled to collaborate with Kigen to pioneer a no-code path to the era of innovative connected products”, remarked Fredrik Wanhainen, CEO of Neue. “Leveraging iSIM resonates with our vision of democratizing IoT with the Neue iEnbl, a complete sensor as service solution and Playground creation environment, empowering our clients to create connected products and services that break new ground.”

“The promise of the integrated iSIM is to unleash secure connected solutions for any company, independent of their experience in cellular,” affirmed Vincent Korstanje, CEO of Kigen.

“Together, we are empowering customers who are building the future of IoT and AI with security that’s ever-present and available out of the box, opening up new frontiers for connected experiences.”

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IoT Analytics published an analysis based on the “Predictive Maintenance & Asset Performance Market Report 2023–2028” report and highlights 5 key insights related to the $5.5 billion predictive maintenance market.

Key insights:

The global predictive maintenance market grew to $5.5 billion in 2022–a growth of 11% from 2021—with an estimated CAGR of 17% by 2028, according to the Predictive Maintenance and Asset Performance Market Report 2023–2028.
With median unplanned downtime costs larger than $100,000 per hour, the importance of accurately predicting failures of large assets has never been higher.
This article shares 5 key highlights of the predictive maintenance market: 1) The market is valued at $5.5 billion, 2) there are 3 different types of predictive maintenance, 3) predictive maintenance software tools share 6 features, 4) predictive maintenance is commonly being worked into the maintenance workflow, and 5) successful standalone solutions vendors specialize in an industry or asset.

Key quotes:

Knud Lasse Lueth, CEO at IoT Analytics, remarks: “Predictive Maintenance continues to be one of the leading use cases for Industry 4.0 and digital transformation, especially in process industries where asset failures can quickly go into the hundreds of thousands of dollars. It is great to see that the market is moving ahead with AI integration into existing APM and CMMS systems and that prediction accuracies are improving. Nonetheless, we still have a long way to go as false alerts remain commonplace.”

Fernando Brügge, Senior Analyst at IoT Analytics, adds that “Predictive maintenance is reaching new heights of maturity and sophistication thanks to the rapid advancements in artificial intelligence, hardware, and data engineering. We are at the point where these technologies enable us to collect, process, and analyze massive amounts of data from multiple sources, and use them to build more accurate and reliable models of machine health and behavior, as well as to determine potential courses of action to fix machine issues. In this way, predictive maintenance is not only a smart way to optimize equipment performance and lifecycle, but also a strategic way to enhance operational efficiency and competitiveness in a rapidly evolving industrial space.”

Predictive maintenance market: 5 highlights for 2024 and beyond

One accurately predicted failure of a large asset is worth more than $100,000 in many industries.

Our latest research highlights, among many other things, that the median unplanned downtime cost across 11 industries is approximately $125,000 per hour. With critical unplanned outages in facilities in industries such as oil and gas, chemicals, or metals occurring several times a year, an investment into predictive maintenance can amortize with the first correct prediction.

Unfortunately, there is a flip side: the accuracy of many predictive maintenance solutions is lower than 50%. This creates headaches for maintenance organizations that often run to an asset to find it is perfectly fine, eroding trust in the entire solution.

That said, vendors have been making strides to increase prediction accuracy, with more data sources and better analysis methods becoming available, including AI-driven analysis. There are positive signs that this determination for better prediction accuracy is helping end users: our research indicates that 95% of predictive maintenance adopters reported a positive ROI, with 27% of these reporting amortization in less than a year.

General search interest in predictive maintenance and related concepts has been on the rise for the last 12 years. Online searches for the term have grown nearly threefold since we initiated coverage on the topic in 2017 and have outgrown condition-based maintenance and asset performance management (APM) related searches.

Indeed, predictive maintenance appears to be well on track to be the must-have killer application we made it out to be in 2021.

In this fourth installment of our predictive maintenance market coverage, we look at 5 important highlights to note about the market going into 2024:

1. The predictive maintenance market is valued at $5.5 billion (2022)
2. There are 3 different types of predictive maintenance, with anomaly detection on the rise
3. Predictive maintenance software tools share 6 common features
4. Integration into the maintenance workflow is becoming important
5. Successful standalone solutions vendors specialize in an industry or asset

Highlight 1: The predictive maintenance market is valued at $5.5 billion

The predictive maintenance market reached $5.5 billion in 2022. Uncertain economic conditions and other manufacturing priorities in the last 2 years resulted in 11% market growth between 2021 and 2022. With companies reinvesting in efficiency, safety, and operational performance, we expect the market for predictive maintenance to grow to 17% per year until 2028.

Our research indicates that industries with heavy assets and high downtime costs are driving the adoption of predictive maintenance solutions (e.g., oil & gas, chemicals, mining & metals).

Highlight 2: There are 3 different types of predictive maintenance, with anomaly detection on the rise

As the market has evolved, 3 noticeable predictive maintenance types have developed:

1. Indirect failure prediction
2. Anomaly detection
3. Remaining useful life (RUL)

The difference between these largely comes down to the objectives, methods of data analysis, and type of output/information they provide. RUL is the hardest to achieve due to resource demands and environmental factors that make it difficult to scale. Indirect failure prediction has been the most used approach, but our research indicates that anomaly detection is on the rise.

1. Indirect failure prediction

The indirect failure prediction approach generally takes a machine health score approach based on a function of maintenance requirements, operating conditions, and running history. This approach often relies on general analysis to yield this score, though supervised machine learning methods can be used if a significant amount of data is available.

Benefits:

Scalability – Indirect failure prediction can be more easily scaled since they rely on equipment manufacturers’ specifications that are more or less the same across machines of the same type.
Cost effective – Indirect failure prediction can use existing sensors and data, reducing the need for additional instrumentation.

Limitations:

Failure time-window accuracy – Indirect failure prediction does not give a timeline of when machines will fail. This can be a problem for organizations with very costly downtimes (e.g., heavy equipment industries).
Dependent on historical data – Indirect failure prediction’s effectiveness relies on the availability of extensive historical data for accurate modeling.

2. Anomaly detection

Anomaly detection is the process of finding and identifying irregularities in the data (i.e., data points that deviate from the usual patterns or trends). While the indirect failure prediction and RUL approaches use failure data to predict future failures, anomaly detection uses the “normal” asset profile to detect deviations from the norm. These deviations can indicate potential problems, such as faults, errors, defects, or malfunctions, that need to be detected and addressed before they cause serious damage or downtime.
This approach makes it easier when there is not a good repository of failure data, and it often relies on unsupervised machine learning.

Benefits

Low data and hardware requirements – Anomaly detection models can identify issues without being trained on failure data. Further, since these models need less data, they do not demand high computing power.
High scalability and model transferability – Anomaly detection models are trained on normal operation data, so they can easily be applied to different machines without retraining or adaptation.

Limitations

Failure time-window accuracy – As with indirect failure prediction, anomaly detection models do not give a timeline of when machines will fail, which can be a problem for organizations with very costly downtimes.
Presence of false positives – While most solutions in the market can distinguish between critical and noncritical anomalies, the choice of unsupervised machine learning models is still important as it can affect how well this distinction can be made (e.g., autoencoders and generative adversarial networks do not capture the complexity of normal operations).

3. Remaining useful life (RUL)

RUL is the expected machine life or usage time remaining before the machine requires repair or replacement. Life or usage time is defined in terms of whatever quantity is used to measure system life (e.g., distance traveled, repetition cycles performed, or the time since the start of operation).

This approach relies on condition indicators extracted from sensor data—that is, as a system degrades in a predictable way, data from the sensors match the expected degradation values. A condition indicator can be any factor useful for distinguishing normal operations from faulty ones. These indicators are extracted from system data taken under known conditions to train a model that can diagnose or predict the condition of a system based on new data taken under unknown conditions.

Predictions from these RUL models are statistical estimates with associated uncertainty, resulting in a probability distribution.

Benefits

Failure prediction time-window – RUL is especially useful for industries where maintenance is very costly and needs advanced planning, such as heavy-equipment industries.
Output robustness – Since RUL estimates rely on high-quality and detailed data, they tend to be more robust and reliable.

Limitations

Resource demand – Training large models requires powerful computing hardware, especially if done on-premises.
Model transferability and scalability – Different environments and usage patterns can cause different failure modes for the same type of equipment. This means the model needs to be retrained for each specific case, reducing its scalability and generalizability.

Highlight 3: Predictive maintenance software tools have 6 common features

Software is the largest segment of the predictive maintenance tech stack, making up 44% of the predictive maintenance market in 2022.

Our report shows that even though most successful predictive maintenance software vendors specialize in industries or assets, there are 6 common features between their various solution software suites:

1. Data collection
2. Analytics and model development
3. Pre-trained models
4. Status visualization, alerting, and user feedback
5. Third-party integration
6. Prescriptive actions

We will delve further into these features and offer an example snapshot for each from various software vendors. The examples are to help readers understand some approaches to these common features.

Feature 1: Data collection

Data collection tools within predictive maintenance software collect, normalize, and store data on asset health/condition parameters. They also collect other data types needed to identify and predict upcoming issues, such as business and process data.

Snapshot:

US-based predictive maintenance software vendor Predictronics offers PDX DAQ, an application that allows users to synchronize data collection from multiple sources for any given period of time. The solution creates a database that harmonizes all the timestamps from different sensors, which Predictronics claims yields the necessary information for analysis and producing real-time, impactful results.

Feature 2: Analytics and model development

Analytics and model development tools within Predictive Maintenance software analyze, interpret, and communicate data patterns, including analytics discovery (e.g., RCA, AD modules) and modeling (e.g., feature engineering and model selection and testing).

Snapshot:

US-based predictive maintenance software vendor Falkonry (recently acquired by IFS) offers Workbench within its Time Series AI platform, a low-code ML-based solution aiming to help users—specifically, operational practitioners, including production, equipment, or manufacturing engineers—discover patterns such as early warning or stages of deterioration in complex physical systems. It also aims to enable users to analyze large amounts of data and build predictive models.

Feature 3: Pre-trained models

Pre-trained models are just that: ready-to-use models typically designed for specific assets in specific industries. These models include capabilities and references for specific assets or failure modes (e.g., fouling for heat exchangers, wear and corrosion for fans, or valve leakage for compressors). These are meant to help end users see examples of models so they can build on them or develop custom predictive maintenance algorithms.

Snapshot:

US-based asset management software vendor AspenTech (recently acquired by Emerson), offers Mtell, an application that includes pre-populated, industry-specific asset templates to help users select sensors for common asset categories and AI functionality to create and deploy models quickly for PdM applications (e.g., for specific compressors, turbines, and blowers).

Feature 4: Status visualization, alerting, and user feedback

Status visualization, alerting, and user feedback tools within predictive maintenance software automatically communicate asset-related data/insights for various personas. These insights often include status dashboards and automatic alerts that trigger work orders or corrective actions, maintenance planning, and optimization. These tools also enable users to provide feedback concerning the accuracy of alerts.

Snapshot:

US-based analytics software vendor SAS Institute offersAsset Performance Analytics, which includes status dashboards and automatic alerts intended to notify operations staff and managers of impending failure so that organizations have time to identify and fix issues before they become costly problems.

Feature 5: Third-party integration

Third-party integration enables users to connect their predictive maintenance software to other software systems and workflow management tools, such as ERP, MES, CMMS, APM (more on APM integration in Highlight 4), and Field Service.

Snapshot:

SKF, a Swedish bearing and seal manufacturing company also offering maintenance products, offers a condition monitoring and predictive maintenance solution that interfaces with existing plant control systems (e.g., MES or SCADA) and other external dashboards (e.g., ERP). It also provides insights to operators in the field via alarms and visualization on handheld devices.

Feature 6: Prescriptive actions

Prescriptive action features typically suggest the optimal actions to take in case of an (upcoming) failure. These actions are typically prioritized by criteria that are set when the algorithm is designed.

The actions that are prescribed by the software vary depending on the nature and urgency of the issue. They may require multiple steps or interventions. For instance, some actions may involve automatically adjusting the equipment parameters or informing the maintenance and operation teams about the necessary procedures to ensure equipment efficiency.

Snapshot:

Marathon, a predictive maintenance software solution from Norway-based Arundo, provides a feature known as Investigations that aims to provide the workflow and instructions to resolve equipment problems according to prescribed corporate standards.

Highlight 4: Integration into the maintenance workflow is becoming important

In its early days, predictive maintenance was mostly a standalone solution developed by startups to address specific customer needs. However, our report highlights a notable trend of sophisticated predictive maintenance solutions integrating into larger APM and computerized maintenance (CMMS) solutions.

APM is a strategic equipment management approach designed to help optimize the performance and maintenance efficiency of individual assets and entire plants or fleets. APM aims to improve asset efficiency, availability, reliability, maintainability, and overall life cycle value.

Various APM vendors are introducing predictive maintenance software tools within their APM offerings. The solutions aim to tie the different capabilities into 1 thread:

Knowing when a machine will fail and mapping how failures could affect production or output
Estimating how much fixing or preventing an issue will cost
Making recommendations on whether it is worth fixing or preventing a problem

By including a sophisticated predictive maintenance solution in an end-to-end asset flow, APM players are trying to become the main partners for their customers’ digitalization journeys.

Our report lists 9 key components of APM:

1. Asset health monitoring
2. Maintenance optimization
3. Reliability analysis
4. Integrity management
5. Performance optimization
6. Failure prediction <- Predictive maintenance resides here
7. Digital asset twin
8. Sustainability management
9. Energy optimization

We assess in our report that improving the failure prediction module of APM solutions is currently one of the key initiatives of leading APM vendors.

Highlight 5: Successful standalone solutions vendors specialize in an industry or asset

Our research found that 30% of predictive maintenance vendors offer standalone, industry- or asset-specific solutions. By tailoring their efforts to specific niches in which they have acquired domain knowledge, they can discern the types of equipment and industries in which their solutions offer the most end-user benefits.

Snapshot:

Israel-based data science company ShiraTech Knowtion uses its equipment expertise in its offering of Predicto, an industrial IoT platform focused on industrial maintenance teams. The platform enables reading and processing of sensor data from production plants, ideally based on its own multisensing devices (iCOMOX). The company has developed specific offerings for motors, pumps, conveyors, and pipes. These asset-tailored offerings enable the company to scale.

6 considerations for predictive maintenance vendors

Six questions that predictive maintenance vendors should ask themselves based on insights in this article:

1. Market growth and strategy: Given the market’s growth to $5.5 billion and the projected increase to $14.3 billion by 2028, how can our company align its strategy to capitalize on this market expansion?
2. Accuracy improvement: Considering the current lower-than-50% accuracy of many predictive maintenance solutions, what innovative approaches or technologies can we adopt to enhance the accuracy of our predictions?
3. ROI communication: How can we better communicate the positive ROI of predictive maintenance to potential customers, especially those who are skeptical due to past experiences with inaccurate solutions?
4. Industry specialization: Given that the most successful vendors are specialized in specific industries, assets, or use cases, should we consider narrowing our focus, and if so, in which areas?
5. Data collection and integration: Are we effectively collecting the right kinds of data (including business and process data) and integrating it into the right IT systems for optimal predictive maintenance?
6. Software tool features: Do our software tools encompass the 6 common features identified in the report (data collection, analytics and model development, pre-trained models, status visualization, third-party integration, prescriptive actions), and are they competitive in the current market?

8 considerations for those looking to adopt or update predictive maintenance solutions

Eight questions that those looking to adopt or update predictive maintenance solutions should ask themselves based on insights in this article:

1. Solution type suitability: Which type of predictive maintenance solution (indirect failure prediction, anomaly detection, or RUL) best aligns with our specific maintenance needs and operational goals?
2. Integration with existing systems: How easily can predictive maintenance solutions integrate into our existing maintenance workflows and asset management systems?
3. Vendor specialization: Should we look for a vendor specialized in our industry, specific assets, or use cases, and how would that benefit us over a generalist provider?
4. Data collection and analysis: Do we have the necessary infrastructure for effective data collection and analysis to support a predictive maintenance system?
5. Accuracy and trustworthiness: How can we evaluate and ensure the accuracy of the predictive maintenance solution to build trust within our maintenance team?
6. Scalability and future growth: How scalable are the predictive maintenance solutions, and can they accommodate our future growth?
7. Software features and functionality: Do the software tools offered by vendors have all the key features we need, such as data collection, analytics, and third-party integration?
8. Market trends and innovation: Given the evolving nature of the predictive maintenance market, how can we stay informed about the latest innovations and ensure that our solution remains cutting-edge?

The post Predictive maintenance market: 5 highlights for 2024 and beyond appeared first on IoT Business News.

According to a new market research report from the IoT analyst firm Berg Insight, the number of micromobility vehicles available worldwide will reach 38.2 million by 2027, up from 25.3 million vehicles in 2022.

Micromobility services are defined as shared mobility services that offer short-term rentals of light vehicles such as bikes, scooters or other similar vehicles. The micromobility market has been characterized by rapid growth, a high pace of merger and acquisitions and many shutdowns of services during the past few years.

“The market is now reaching a more mature stage and operators are generally more focused on reaching profitability, which was not the case in previous years”, said Martin Cederqvist, IoT Analyst at Berg Insight.

Bikesharing is a decentralised bicycle rental service, usually focusing on short term rentals. Traditionally, most bikesharing operators have used station-based operational models. This operational model requires members to pick up and return the vehicle at any designated station within a city. In Europe and North America, station-based bikesharing is the most popular operational model. Another model that is rapidly gaining in popularity is free floating services, which enables members to pick up and drop off vehicles anywhere within a designated area. The total number of shared bikes worldwide reached an estimated 23.7 million vehicles at the end of 2022, of which a large majority are free floating bikes in China. Prominent bikesharing operators include Hellobike, Meituan Bike, Didi (Qingju), JCDecaux, Nextbike (owned by Tier), Hello Cycling, Docomo Cycle, Anywheel, Yulu, RideMovi, Bolt, Lime and Donkey Republic.

The stand-up scootersharing market has gained a lot of attention from media and investors and have grown rapidly since its inception in 2017. At the end of 2022, the number of shared stand-up scooters reached an estimated 1.5 million vehicles.

“Leading stand-up scooter operators include Bolt, Tier and Voi in Europe, Bird and Lime in North America as well as Neuron Mobility, Swing Mobility and Beam Mobility in Asia-Pacific”, said Mr Cederqvist.

The regulatory environment surrounding stand-up electric scooters is complex and varies between region, country, state and city level. Today, some cities limit the number of stand-up scooters allowed on the streets through mandatory operator licences. The cities can both restrict the number of operators allowed in the cities, but also the number of vehicles each operator is allowed to deploy. The sit-down scootersharing market has not been regulated to the same degree. At the end of 2022, the number of shared sit-down scooters in shared mobility schemes reached an estimated 120,000 vehicles. Leading sit-down scootersharing operators include Vogo and Yulu in India; Marti Technologies in Turkey; Cooltra, CityScoot, Felyx, Emmy and Check in Europe; Revel in North America as well as GoShare and WeMo in Taiwan.

Telematics has been a key element since the inception of shared micromobility services. Bikesharing infrastructure vendors provide complete solutions including telematics hardware solutions, user identification and bike locks, information kiosks as well as fleet management platforms and mobile apps.

Companies specialising in bikesharing solutions include PBSC (owned by Lyft), Lyft, Nextbike (Tier), Fifteen, Vaimoo, Youon Bike Technologies and Conneqtech. Scootersharing operators now mainly utilise factory-installed telematics systems embedded in the scooters. Shared mobility software platforms moreover comprise complete systems that can support all the operational activities of a micromobility operation ranging from management of in-vehicle equipment, fleet management, booking management, billing as well as operations supervision via dashboards and data analytics. Leading micromobility telematics solution providers include Comodule, Drover AI, Luna Systems, Invers, Teltonika, Queclink, Atom Mobility, Joyride Technologies, Wunder Mobility, Zoba, Urban Sharing and Omni Intelligent Technology.

Mr Cederqvist concluded:

“In recent years, micromobility operators have added increasingly complex telematics applications such as sidewalk riding detection as well as features ensuring parking compliance. The usage of AI and machine learning models are increasingly common in such applications.”

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The number of active fleet management systems deployed in commercial vehicle fleets in Australia and New Zealand was around 1.4 million units in Q4-2022 according to a new research report from the leading IoT analyst firm Berg Insight.

Growing at a compound annual growth rate (CAGR) of 11.5 percent, this number is expected to reach more than 2.4 million units by 2027. A large number of vendors are active on the fleet management market in Australia and New Zealand. The top-15 players in the region together account for over 60 percent of the active units on the market, and more than a third is represented by the top-5 alone.

A wide variety of players serve the fleet telematics market in Australia and New Zealand, ranging from small local vendors to leading international solution providers.

“Berg Insight ranks Teletrac Navman, EROAD and MTData as the largest providers of fleet management solutions in Australia and New Zealand”, said Rickard Andersson, Principal Analyst, Berg Insight.

US-based Teletrac Navman (part of Vontier) was the first to reach 100,000 units in the region and this milestone has now also been achieved by New Zealand-based EROAD (including Coretex acquired in 2021) and Australia-based MTData (owned by Telstra).

“The remaining top-5 solution providers in the region are US-based Verizon Connect and Netstar Australia”, continued Mr. Andersson.

He adds that US-based Rand McNally which acquired Fleetsu in Australia in 2022 is now also a significant player. Other notable vendors with comparably sizeable subscriber bases in the region include local solution providers such as Australia-based IntelliTrac and Linxio and New Zealand-based Smartrak (Constellation Software), as well as international players including Canada-based Geotab and South Africa-based MiX Telematics. Fleet Complete, also based in Canada, entered the region through the acquisition of Geotab’s reseller Securatrak.

“Additional top-15 players in the region are Digital Matter and Procon Telematics as well as Bridgestone Mobility Solutions’ Webfleet and Fleetdynamics by Fleetcare”, said Mr. Andersson.

Solution vendors just outside of Berg Insight’s top list moreover include Directed Technologies (Directed Electronics Australia), Sensium, Inseego, TrackIt and Microlise. Directed notably works with a large number of commercial vehicle OEMs on the local market.

“OEMs which have launched fleet telematics solutions in the region independently or through partnerships include UD Trucks, Toyota, Hino, Mitsubishi, PACCAR, Volvo Group, Daimler Truck, Fuso, Scania, MAN and Iveco”, concluded Mr. Andersson.

The post The installed base of fleet management systems in Australia and New Zealand will exceed 2.4 million units by 2027 appeared first on IoT Business News.

Quectel Wireless Solutions, a global IoT solutions provider, is pleased to announce a major milestone for its satellite communication module, CC200A-LB, which has recently secured global certifications from leading authorities, including CE, FCC, IC, and RCM.

This confirms CC200A-LB’s compliance with satellite network standards across Europe, North America, Canada, Australia, and New Zealand.

With a focus on cost-effectiveness and ultra-low latency, the CC200A-LB module delivers a reliable and uninterrupted global network connection. This makes it an ideal solution for a wide range of applications, including maritime, transportation, heavy equipment, agriculture, mining, oil and gas monitoring as well as other scenarios where cellular networks may be limited.

Norbert Muhrer, President and CSO, Quectel Wireless Solutions, said:

“We are thrilled to see the global approval of our satellite communication module. Its capability in delivering uninterrupted network coverage ensures continuous and cost-effective communication for managing remote and mobile assets anytime, anywhere.”

The CC200A-LB incorporates cutting-edge satellite IoT connectivity offered by ORBCOMM, utilizing the L-band of the Inmarsat GEO constellation. It features two-way communication, low latency, and global coverage. When paired with cellular modules, it enables distinctive dual-mode IoT applications, providing unparalleled reliability, redundancy and ubiquitous coverage. In scenarios with inadequate or disrupted cellular network coverage, IoT devices can seamlessly sustain communication through satellite connections.

The CC200A-LB is designed with a streamlined LCC+LGA package, measuring 37mm × 38mm × 3.35mm. Equipped with multi-constellation GNSS positioning, the module can identify the device’s location quickly and precisely. Its user-friendly AT command set facilitates effortless configuration and management.

The module can be purchased in isolation or with the appropriate Quectel antenna to help accelerate the time-to-market of customer devices.

Apart from the CC200A-LB, Quectel has diversified its portfolio by introducing a range of satellite communication modules. Among them, the CC660D-LS is compliant with 3GPP NTN R17 standards and features two-way communication, support for multiple frequency bands, low latency as well as low power consumption. The BG95-S5 and BG770A-SN modules are designed to support both satellite and cellular networks, providing ubiquitous coverage for IoT applications.

The post Quectel’s Satellite Communication Module CC200A-LB Achieves CE, FCC, IC, and RCM Certifications appeared first on IoT Business News.

The PCBA (Printed CircuitBoard Assembly) is the core of electronic devices, combining industry know-how and the assembly of electronic components, which is a pivotal process for device manufacturers in the early stage of product design. By offering the customized PCBA solutions to smart terminal devices such as POS (Point-of-Sale), PoC (Push-to-Talk over Cellular), wearable cameras and more, Fibocom can greatly simplify the manufacturing process, improve efficiency and reduce the time-to-market for industry customers.

Fibocom, a global leading provider of IoT (Internet of Things) wireless solutions and wireless communication modules, announces the fulfillment of its PCBA capability for industry customers in the field of smart payment, industrial handheld, connected AI camera, commercial display, intelligent service robotics, AI edge server, etc.

Encompassing hardware design, MD & ID evaluation, algorithm migration & integration, certification, and software application support, the comprehensive PCBA solution drastically expedites the commercialization of customers’ smart devices.

“Driven by the surge in digital transformation across industries, the demand for higher product quality, efficiency, time-to-market, cost-effectiveness, and software-hardware services is burgeoning,” said Ralph Zhao, VP of MC BU at Fibocom. “Leveraging Fibocom’s rich experience in the vertical markets and industry know-how accumulated for more than 24 years, the PCBA solutions are positioned to accelerate digitalization across industries with tailored, high-performance integration. We are confident in assisting our customers to win the market sectors in smart retail, PoC, and wearable cameras through a comprehensive of innovative solutions.”

Enabling Rich Functionalities for POS (Point-of-sale)

Smart payment terminals are evolving into highly integrated devices that adopt various exterior layouts and support rich payment methods such as contactless, QR codes, card payments, etc. Fibocom provides a tailor-made PCBA solution that enables reliable wireless connectivity for POS terminals and supports 720/1080PHD touchscreens with a powerful CPU integrated. In addition to the support of the Android operating system, it allows retailers to upgrade the software through the entire product life cycle and develop the industry applications continuously.

Enabling Robust Roaming and Multimedia Capabilities for PoC (Push-to-Talk over Cellular)

Addressing the requirements for PoC terminal devices, Fibocom offers a comprehensive PCBA solution to reduce the complexity for PoC manufacturers at the early stage of product design. Integrating the cellular capability, multimedia functions, communication protocols, operating systems, and GNSS into the PCBA at a compact size, significantly expedites the timeline of the IoT project, reduces development complexities, and achieves faster time-to-market.

Enabling HD Streaming for Wearable Cameras

The popularity of wearable devices has raised the standard of connectivity and audio-video streaming capabilities, Fibocom’s PCBA solution supports the global 4G/5G cellular roaming, along with Bluetooth/Wi-Fi/NFC for short-distance communication. Additionally, the high-performance graphic engine supports there cording and playback of 4K/1080P video, multi-camera input and output, infrared night vision as well as H.264/H.265 decoding. Leveraging the reliability and scalability of wearable devices, the PCBA solution also provides flexible selections on data storage and peripheral interfaces. The solution can be widely deployed in inspection patrol, transportation, live broadcasting, etc.

The post Fibocom Propels Digitalization of Smart Industry Terminals with Customized PCBA Solutions appeared first on IoT Business News.

Highlights:

Leading edge solutions built around Globalstar IoT satellite technology are helping conservationists in Africa to monitor and protect near-extinct rhinoceros from poachers
Low-cost ST100 chipset is at the heart of wildlife tracking platform provided by specialist Globalstar Value Added Reseller Wintec Solutions
Satellite technology means tracking of free-roaming animals is ubiquitous and transmissions uninterrupted, even when beyond GSM mobile networks’ reach

Globalstar Europe Satellite Services Ltd., a wholly owned subsidiary of Globalstar, Inc., the next-generation international telecommunications infrastructure and technology provider, announced today that its IoT (internet of things) satellite technology is being used to protect multiple endangered species of wildlife in Africa.

Leading-edge satellite-enabled tracking devices incorporating Globalstar’s small and lightweight ST100 chipset are being used to monitor rhinoceros, part of a widespread concerted effort to protect these nearly-extinct wild animals from illegal poachers.

This is the most recent project carried out by Globalstar Value Added Reseller, Johannesburg-based Wintec Solutions, a specialist technology and solutions provider with extensive experience in animal tracking for wildlife conservation.

Rhinoceros are the subject of widespread illegal killing by poachers across Africa. Wintec Solutions deployed GSatSolar, the latest addition to Globalstar’s IoT satellite tracking solutions portfolio. GSatSolar is an innovative Sun-powered tracking device built around Globalstar’s economical ST100 satellite IoT transmitter. Low-cost and simple to integrate, GSatSolar is a small and rugged satellite IoT tracker ideal for wildlife monitoring. Rhinos are fitted with the device via ear tagging or collar. Other novel ways of deploying the devices are also being explored. The device is also suitable for livestock tracking.

Nature reserves in Africa are expansive, spanning as many as 20,000 hectares in remote bush regions. Their perimeter fencing is often breached by criminals and poachers attempting to gain access to, and kill, the animals within. Such reserves, which are home to as many as 200 free-ranging rhinos, typically are established in locations where GSM mobile signal does not exist. Only satellite communications can provide the always-on connectivity to enable uninterrupted monitoring of the animals.

Significant advantages of GSatSolar are its small size and light weight, so as not to irritate the animal, as well as its robust structure. Furthermore, its low cost makes the solution easily scalable for tracking large herds of wildlife and farm stock. “Its physical attributes make GSatSolar a game changer, and transform the possibilities for wildlife conservation and livestock management,” says Rene Winter, Director of Wintec Solutions.

Globalstar-enabled solutions are at the forefront of today’s conservation activities in Africa; Wintec Solutions is additionally playing a role in the preservation of other African species. Researchers at Giraffe Conservation Foundation are using the system to monitor giraffe migration, and for keeping safeguarding tabs on animals which have been reintroduced into the wild. “We are currently trialling GSatSolar ear tags on giraffe in Namibia. The first units were deployed in June this year and, while field trials are still ongoing, the data received so far are very promising. We’re looking forward to deploying more units in the future and testing them in different countries,” said Dr Courtney Marneweck, Science Data Coordinator at Giraffe Conservation Foundation.

Wintec Solutions is also deploying satellite technology and Globalstar-enabled animal tags in collaboration with veterinary authorities and research students for wild buffalo protection activities in South Africa’s Kruger National Park. Buffalo migration is being monitored so conservationists and government authorities can understand and mitigate the potential spread of Bovine Tuberculosis. In another project, the application of Globalstar-enabled trackers is being tested on pangolins, yet another severely endangered species.

The tracking platform UI developed Wintec Solutions includes an informative mapping portal that tracks the animals’ individual and group movements. Monitoring the rhino, or other wildlife, also informs as to grazing habits, locations of the best nutrition, seasonal breeding habits, as well as the animals’ general health and well-being.

Winter said:

“Our wildlife preservation activities take place well beyond the reach of GSM mobile; Globalstar’s satellite network, with its ubiquitous reach and reliability, is a fundamental enabler in the operations being carried out to protect endangered species.”

“Globalstar created the ST100 transmitter to meet the widespread need for easy-to-integrate, low-cost and lightweight monitoring devices that leverage satellite technology and the Internet of Things,” commented Mark O’Connell, General Manager at Globalstar EMEA & APAC. “We are absolutely delighted that solutions developed through the inventiveness and commitment of our partner Wintec Solutions are now playing such a pivotal role in the vital wildlife conservation activities today taking place across Africa,” he added.

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