This is the fourth article in a 6-part series exploring the technologies enabling the cutting-edge IoT.
Maintenance, Repair, and Operations (MRO) efficiency is one of the key contributors to profitability in industrial companies. This is why modern industrial enterprises are heavily investing in smart ways to manage their physical assets. Specifically, intelligent asset management helps enterprises to optimize maintenance schedules, maximize asset lifetime, and minimize physical wear.
These optimizations lead to improved productivity and positively impact their bottom lines. Nowadays, enterprises are offered various models for managing their assets. These models vary in complexity and deliver different levels of efficiency in the industrial automation domain.
Once upon a time, assets were maintained in a reactive fashion i.e., based on a reactive maintenance paradigm. A reactive maintenance approach maintains or repairs an asset when the latter stops functioning. This approach maximizes the utilization of the asset since the asset is used till its End-of-Life (EoL). However, this means that the operations that depend on the asset (e.g., production processes) must stop, and the company must deal with catastrophic unplanned downtimes. For example, production stoppage has adverse impacts on the continuity of industrial operations and the ability of the enterprise to meet productivity targets.
The downsides of reactive maintenance have led industrial enterprises to employ a preventive approach to asset management, namely preventive maintenance. This paradigm maintains assets before their EoL to avoid unplanned downtime. In this direction, the asset's nominal EoL as given by the OEM (Original Equipment Manufacturer) is considered.
Preventive maintenance enables industrial organizations to ensure the continuity of their processes as maintenance schedules can be properly planned at times that do not disrupt their operations. Nevertheless, preventive maintenance is far from being the best possible asset management model. This is because assets are underutilized and the Overall Equipment Efficiency (OEE) of the enterprise ends up being sub-optimal.
To alleviate the limitations of preventive maintenance, companies are implementing condition-monitoring and condition-based maintenance (CBM) of assets. Rather than relying on predetermined values of the asset's nominal EoL to schedule the maintenance, CBM leverages the actual condition of the asset to determine whether it should be maintained, replaced, or repaired.
To this end, CBM analyzes data that reflect the actual physical condition of the asset, such as information about the asset's operating vibration and temperature, as well as information from the asset's energy consumption or oil analysis. CBM facilitates maintenance engineers to identify issues with the assets (e.g., failures, wear) in an accurate and timely fashion. As such, it can also enable fast and effective maintenance processes.
Predictive maintenance (PdM) is one of the most prominent and advanced cases of CBM. It analyzes information about the condition of the asset in order to accurately predict its Remaining Useful Life (RUL). Specifically, RUL predictions are derived based on predictive analytics algorithms over sensor data, such as data from vibration, temperature, acoustic, and ultrasonic sensors, as well as thermal images. RUL estimation is a foundation for optimal scheduling of the assets' maintenance, which leads to the best possible OEE. In this way, PdM delivers tangible benefits to enterprises, including improved utilization of assets, as well as high-quality and cost-effective operations.
Predictive maintenance is currently considered one of the most prominent use cases of the fourth industrial revolution (Industry 4.0) since it is relevant to a wide array of industrial sectors and its critical infrastructure including manufacturing, energy, oil & gas, mining, smart cities, smart buildings, and facilities management. Specifically, PdM is extensively used in industrial automation applications in manufacturing shopfloor, oil refineries and energy plants. In recent years, PdM finds more and more applications outside of industrial automation, such as building automation for Smart Buildings".
Technology Building Blocks for Predictive Maintenance Systems and Services
The development and deployment of CBM and PdM systems hinge on the integration of high-fidelity sensing systems with state-of-the-art digital technologies. In particular, a PdM system comprises the following technological elements:
These modules and technology building blocks are combined in the scope of end-to-end solutions. Integration can be challenging as the above-listed blocks are not always compatible with each other. For instance, there is usually a mismatch in the data models produced by the hardware and the models that are consumed by the machine learning algorithms. In this direction, companies are better off using PdM platforms that alleviate this heterogeneity and ensure the interoperability of the various modules.
At the hardware level, there are platforms and maintenance kits that integrate different sensors in a hardware box. In practice, the development of end-to-end solutions are also challenging from an economic perspective. This is because an integrated end-to-end solution involves the licensing and use of elements from different providers, including hardware providers, cloud infrastructure providers, and software/analytics vendors.
Practical Applications of Predictive Maintenance
Using the above-listed technologies it is nowadays possible to develop a wide range of PdM-based applications, which help industrial enterprises improve the quality, timeliness, and the cost-effectiveness of their operations. Here are some popular use cases:
As a leading semiconductor manufacturer, Infineon is offering several cutting-edge predictive maintenance solutions, including hardware, simulation, and analytics offerings.
This predictive maintenance kit enables the development of end-to-end sensor-enabled condition monitoring and predictive maintenance solutions. The kit is the result of a unique partnership of Infineon along the IoT value chain. It enables the general development of popular use cases such as condition monitoring and predictive maintenance, e.g. for HVAC equipment in smart buildings or pumps in industrial automation. In this direction, the kit leverages Infineon's broad XENSIV™ sensor portfolio along with the FreeRTOS XMC4700 Arm® Cortex®-M4F XMC™ microcontroller qualified device. This sensor portfolio supports the collection of a very wide range of physical parameters from different assets, including:
Based on these sensors and the collected data, industrial organizations can implement various predictive maintenance scenarios. For example, in the case of HVAC maintenance, the XENSIV™ sensor portfolio enables the collection of condition data about various elements of HVAC units such as filters, compressors, motors, or fans. Furthermore, the XENSIV™ predictive maintenance evaluation kit facilitates the management of sensor data in the cloud, as well as their analysis by means of advanced predictive analytics and machine learning techniques.
Infineon provides a novel Lifetime Estimation service for industrial assets. It is an enterprise-scale 24/7 service that leverages lifetime estimation algorithms and Infineon's expertise to facilitate the design process. Customers of this service can therefore benefit from Infineon's expertise in power electronics in order to simplify and accelerate their design processes.
The IPOSIM service offers access to three different simulation types including:
The service is accessible through a user-friendly Graphical User Interface (GUI). The GUI guides designers in a step-by-step process through the simulation with power devices. Overall, the IPOSIM services facilitate selection of the most suitable high-power products for a given set of application requirements. Moreover, it helps reducing development costs, optimizing product sizing, reducing the bill of materials (BOM), and accelerating time-to-market.
Infineon-owned Industrial Analytics provides AI (Artificial Intelligence) based analytics services for intelligent asset management applications. Specifically, the company offers access to advanced analytics services for plant monitoring and timely detection of assets' degradation based on various sensing modalities such as vibration sensing. The analytics solutions include predictive maintenance based on predictive analytics functions, as well as the provision of prescriptive recommendations about how to best maintain, service, and repair industrial assets. The offered services take advantage of Industrial Analytics AI expertise and Infineon's deep knowledge of the semiconductors industry.
Overall, Infineon's solutions span all the different elements of predictive maintenance solutions: From reliable sensing to advanced AI-based analytics. The solutions are scalable, integrated, and easy to deploy. This helps industrial enterprises to accelerate the development and deployment of a wide range of condition monitoring and predictive maintenance in a variety of configurations.
Learn more about condition monitoring and predictive maintenance solutions here.
Infineon Technologies AG is a world leader in semiconductor solutions that make life easier, safer and greener. Microelectronics from Infineon are the key to a better future. With around 50,280 employees worldwide, Infineon generated revenue of about €11.1 billion in the 2021 fiscal year (ending 30 September) and is one of the ten largest semiconductor companies worldwide. To learn more click here.
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