The use of Silicon Carbide (SiC) MOSFETs significantly improves converter performance by increasing efficiency and reducing costs, to the detriment of electro-magnetic emission and reliability. Implementing a predictive maintenance strategy based on a prognosis tool can mitigate this limitation. This literature review offers a methodological synthesis of prognosis design tools for SiC MOSFETs, while also encompassing studies on IGBTs and silicon-based power MOSFETs where these approaches are transferable. The analysis focuses on wear-out prognosis under nominal operating conditions of standard package device, excluding environmental constraints. Articles published up to 2025 were identified in the OpenAlex database using a keyword-based search and manually filtered according to the study scope. Most reviewed works rely on Data-Based prognosis methods, mostly based on neural networks, though out-of-sample validation remains uncommon. Our study also highlights the dependence of Data-Based prognosis performance on the shape of degradation indicator trends. Moreover, the estimation of prediction uncertainty is rarely addressed in the reviewed literature. Despite notable methodological advances, ensuring the reliability of prognosis tools for SiC MOSFETs remains an ongoing research challenge.

Metallized film capacitors are often responsible for failures in electronic systems. Predicting their lifetime to anticipate such failures is vital for assessing the reliability of these systems. This study presents accelerated aging tests involving voltage and temperature on 27 capacitors. The aim is to enhance the existing database and analyze the behavior of the resulting curves. A post-mortem analysis is carried out to evaluate the failure mechanisms. Based on this analysis, a new model is introduced. This model is evaluated against the data and compared with current laws in the literature and functions describing the experimental curves. The results confirm the model’s effectiveness, and its predictive capability is discussed.

Induction motors make up approximately 80% of the electric motors in the railway sector due to their robustness, high efficiency, and low maintenance cost. Nevertheless, these motors are subject to failures which can lead to costly downtime and service interruptions. In recent years, there has been a growing interest in developing fault diagnosis systems for railway traction motors using advanced non-invasive detection and data analysis techniques. Implementing these methods in railway applications can prove challenging due to variable speed and low-load operating conditions, as well as the use of inverter-fed motor drives. This comprehensive review paper summarizes general methods of fault diagnosis for induction machines. It details the faults seen in induction motors, the most relevant signals measured for fault detection, the signal processing techniques for fault extraction as well as some classification algorithms for diagnosis purposes. By giving the advantages and drawbacks of each technique, it helps select the appropriate method that could address the challenges of railway applications.

Metallized film capacitors are a common cause of electronic system failure. Since some of their failures can lead to serious accidents and severe damage to their surroundings, such as explosions or fires, their fail-safe should be ensured independently of their intern structure. The aim of this article is to investigate the runaway process leading to these catastrophic failures. Non-destructive accelerated aging tests to prevent unusual failures are carried out on 42 capacitors. Their key characteristics are measured periodically. Runaway indicators are found and analyzed. Based on the results, knowledge about the runaway process is deepened. Furthermore, a solution appears to avoid the catastrophic failures.

Metallized polypropylene film capacitors are known to be one of the most common causes of failure in electronic systems. Predicting their lifetime to anticipate failures is a key issue in the assessment of these systems' reliability. In this paper, accelerated ageing tests applying voltage, temperature and humidity were conducted on 42 capacitors. The aim is to evaluate and sharpen the existing laws in literature thanks to the support of these data. Therefore, based on an analysis of failures and the understanding of this phenomenon, a new law is introduced modelling the capacitance degradation. The model has been assessed against the data, as compared to present laws in literature and other functions, describing the evolution of experimental curves.

Rotating machines are widely used in several fields such as railways, renewable energies, robotics, etc. This diversity of application implies a large variety of faults of critical components susceptible to fail. For this purpose, prognostics and health management (PHM) is deployed to effectively monitor these components through the detection, diagnostics as well as prognostics of faults. In the literature, there exist numerous methods to ensure the above monitoring activities. However, few of them consider different failure types using heterogeneous data and various operating conditions. Also, there are no dominant methods that can be generalized for monitoring. For this reason, the genericity of these methods and their applicability in several systems is a crucial issue. To help researchers to achieve the above challenges, this paper presents a detailed description of data sources from experimental test benches. These data-sets correspond to different case studies that monitor the health states of multiple critical components in various operating conditions using numerous sensors.

This paper focuses on classification methods for evaluating the lifetime consumption (LC) of power electronics modules. The generalization of power electronics devices introduces new issues concerning the reliability of equipment, especially in the transportation field. To meet these expectations, this paper discusses an approach to evaluate the percentage of lifetime of a lab-scale SiC MOSFET power module, designed for an aircraft application. This module is based on a planar technology, and presents typical failure modes concerning the SiC MOSFET chip itself and its environment. The modules have been aged on a specific instrumented test bench to trigger the expected failure modes. Thanks to it, a large database of parameters have been obtained in order to find a relevant failure signature. Once the signature obtained, a comprehensive solution is required to classify the signatures into relevant classes related to the module LC. To meet the issue, three types of classification have been tested with learning data set: Support Vector Machine, k-Nearest Neighbors and neural network. The last contribution of this paper is a discussion on the evaluation of the percentage of lifetime consumption of a new test module thanks to the most promising models obtained from the learning data set.

Junction temperature estimation is a crucial topic for assessing the state-of-health of semiconductors. In this work a measurement board provides an initial estimation of the temperature. Afterwards, a Kalman filter improves this estimation. The ALS method is used for obtaining an optimal performance of the Kalman filter. Finally, the proposed methodology is deployed in an experimental setup and the temperature estimation is validated with the help of a real measurement through fiber optics sensors.

In today’s industrial environment, effectively monitoring assets throughout their entire lifetime is essential. Prognostic and Health Management (PHM) is a powerful tool that enables users to achieve this goal. Recently, sensor-equipped actuator electrification has been introduced to capture intrinsic key system variables as time series. This data flow has opened up new possibilities for extracting essential maintenance information. To leverage the full potential of these data, we have developed a novel algorithm for time series registration, which serves as the core of a new similarity-based prognostic method in a PHM context: Partial Time Scaling Invariant Temporal Alignment for Remaining Useful Life Estimation (PARTITA-RULE). Our algorithm transforms acceleration signals into a subset of descriptors for a new actuator, creating a time series. We can extract valuable maintenance information by aligning this time series with the one already labeled from past behaviors of the same actuator’s family of heterogeneous sizes and robust scaling factors. The unique aspect of our method is that we do not need to inject prior knowledge for registration intervals at this stage. Once the unknown series is aligned with all possible candidates, we create a weighting scheme to assign a relevance score with an uncertainty measurement for each aligned pair. Finally, we compute interpolants on the Wasserstein space to obtain the asset’s Remaining Useful Life (RUL). It is important to note that a relevant result in a PHM context requires a database filled with different labeled system behaviors. To test the effectiveness of our method, we use an industrial data set of vibration signals captured on an aeronautical electric actuator. Our method shows promising Remaining Useful Life (RUL) estimation results even with incomplete time segments.

During their lifetime, wire harness can be modified due to external environment or unintentional technical action. It is therefore possible to see changes in the overall system behavior thus compromising system safety and reliability. There is, consequently diagnosis and maintenance issues. In modern cars, the electrical wire harness verification during manufacturing and afterwards maintenance and diagnosis become crucial due to increase of complexity and functional safety requirement. Estimating the line impedance (resistance and inductance) is beneficial to validate the wire harness during manufacturing and also to evaluate its health state over the vehicle lifetime. Integrate a very low wire inductance measurement system in one chip with reasonable area is a strong challenge. Because, a very small variation of below 0.1µH over a wide range of line inductance is desired. Then, measurement sensitivity requirement is very high. This paper describes a semi-conductor integrable method for measuring the wire inductance and detect a relative variation of wire length of about 10cm over 6m.

Induction motors have numerous advantages due to their robustness and their power–weight ratio [...]

During the design phase, target reliability values of components - including semi-conductors - allow the computation of converter level reliability and redundancy requirements. This work proposes a more accurate method based on manufacturer lifetime models and mission profile evaluation. The new method is applied on a Modular Multilevel Converter.

Partial discharges (PD) measurement is the most common technique to identify defects and the corresponding risks for Medium/High Voltage equipment. However, the methods of recognition developed under HVAC constraints must be adapted under DC. In that purpose, recognition methods based on PD pulse magnitude and pulse time sequence are being studied. A machine learning tool, with integrated Artificial Intelligence software, has been developed to analyze and recognize defect under DC voltage. The software compares several classification methods to diagnose different type of PD defects and allows to select the best one for recognition. To build PD database and to verify the performance of the proposed software, partial discharge measurements were performed with several types of defects (protrusion on HV conductor, particle on insulation and floating-moving particle) placed in a real size SF6 Gas Insulated Switchgear (GIS) filled at several pressures. Part of the database has been used for learning and the remaining data to evaluate recognition accuracy. The results show more than 98 % accuracy for all investigated defects. The learning database can be smaller than the validation one showing the reliability of results. Nevertheless learning data must be sufficiently various to be representative of most situations.

Power converters’ usage is expanding ever in industrial applications as they provide flexibility, high level of performances and new functionalities. However, with increased complexity come new constraints with respect to reliability. This chapter covers a study on reliability of a lab-scale power electronic module taken here as a vehicle. The downsizing of converters and new application-related operating constraints are accompanied by an increase in current density. The use of Silicon Carbide wide-gap technology in power modules is therefore attracting but remains a challenge because this technology is not yet mature and does not benefit from the deep knowledge established about Silicon counterpart. Therefore, health monitoring has naturally emerged as an effective way to implement a reliability assessment. After a brief description of the expected failure modes, an experimental failure monitoring bench will be presented. The choice and implementation of failure indicators through a classification using a neural network will be discussed and presented.

The aerospace industry develops prognosis and health management algorithms to ensure better safety on board, particularly for in-flight controls where jamming is dreaded. For that, vibration signals are monitored to predict future defect occurrences. However, time series are not labeled according to severity level, and the user can only assess the system health from the data mining procedure. To that extent, a clustering algorithm using a deep neural network core is developed. Time series are encoded into pictures to be fed into an artificially trained neural network: U-NET. From the segmented output, one-dimensional information on cluster frontiers is extracted and filtered without any parameter selection. Then, a kernel density estimation finally transforms the signal into an empirical density. Ultimately, a Gaussian mixture model extracts the latter independent components. The method empowered us to reveal different degrees of severity faults in the studied data, with their respective likelihoods, without prior knowledge. It was then compared to state-of-the-art machine learning algorithms. However, internal clustering results evaluation for time series is an open question. As the state-of-the-art indexes were not producing relevant results, a new indicator was built to fulfill this task. We applied the whole method to an actuator consisting of an induction machine linked to a ball screw. This study lays the groundwork for future training of diagnosis and prognosis structures in the health management framework.

In the More Electric Aircraft (MEA), many subsystems that previously used hydraulic, mechanical, and pneumatic power have been fully or partially replaced with electrical systems. Particularly, this article discusses the feasibility of an equipment intended for the electric taxiing application. Namely a 35 kW DC-DC converter with 2500 Wh storage unit targets the recovery of energy from aircraft electrical brake and restitution on the network. First, the main technical and electrical characteristics of the converter prototype are described. The design of the battery pack is detailed. A 35 kW interleaving multilevel converter appears to be a good candidate to fit with the aeronautic constraints. The total mass of such equipment is 68 kg while volume is 72 L.

This chapter discusses how the diagnosis and prognosis tools can be integrated with the design and operation of a drive for high reliability. It also discusses the sources of reliability information about components, how this information can be included in the reliability estimation, and how prognosis can lead to a more reliable drive. The chapter describes additional metrics based on the costs associated with the operation and management of a fault, and provides new metrics of reliability. It covers the reliability of subsystems and systems, reliability prediction, lifetime prediction, and fault management and mitigation. The chapter presents the model of the electric propulsion system operation as a stochastic model of multi-state system reliability with the change of discrete operating modes: start, acceleration, constant speed, deceleration, and stop. It also provides the details of scheduled maintenance, condition-based maintenance and prognosis-enhanced reliability.

Power electronics addresses an ever-growing market with challenges in many fields as in transportation and aerospace. Power electronic conversion is one of the most appropriate way to reduce weight and volume in systems, especially in severe environment like in aeronautics. Power converters are modular and basically constituted of power semiconductor modules. It is well-known that the reliability of the whole system is strongly affected by the power module performances. In the perspective of achieving high reliability power converters, the online condition monitoring (CM) is a promising approach to detect aging and failure issues in power modules. This article proposes to investigate the role of a passive sensor, i.e. a strain gauge, to be considered in the design of planar power module (without bonding) based on Silicon Carbide (SiC) MOSFETs. The sensor is combining electrical, thermal and mechanical health indicators. After an analysis of failure modes expected in a lab-scale power module, an experimental test bench for measuring such indicators is introduced. Signatures of failures are analyzed in accordance with failure occurrences. Particularly, initial results using a strain gauge are discussed and compared to other sensor responses (especially based on VdsON, voltage between the drain and the source of the transistor when conducting). Promising results are established as well as limitations and future work.

Electromechecanical actuators in the aerospace industry are gradually replacing hydraulic ones. In these circumstances, prognostics and health management are innovative frameworks to ensure better safety on board, especially in flight controls where jamming is dreaded. It allows the user to assess and predict system health in real-time. The first step is to collect temporal data from the monitored actuator and perform a data mining procedure to gain insight into its current health. Clustering encompasses several data-driven methods used to reveal patterns. However, getting a set of classes usually requires providing the algorithm with prior knowledge, such as the number of groups to seek. To avoid this drawback, we have developed a clustering algorithm using a deep neural network, as its core, to get the number of groups in data associated with their likelihood. Temporal sequences are reshaped into pictures to be fed into an artificially trained neural network: U-NET. The latter outputs segmented images from which one-dimensional information is extracted and filtered, without any need for parameter selection. A kernel density estimation finally transforms the signal into a candidate density. This new method provides a robust clustering result coupled with an empirical probability to label the times series. It lays the groundwork for future training of diagnosis and prognosis structures in the PHM framework.

The process of determination of batteries lifespan for a specific application generally contains some aging tests. Most of the time, these tests used simplified power or current profiles designed specially to be as representative as possible of the reality. The use of a real profile could improve the reliability of the approach but needs to know how to select the typical profile from an available database. This article shows the methodology used to analyze a database of 10 electric vehicles monitored during 2 years. Different mathematical methods (Pearson and Spearman coefficient of correlation, Shannon entropy, principal component analysis, K-means, ...) were applied to sort the essential variables and classify the uses of the vehicle. Representative classes of uses were then gathered to constitute a profile used in aging tests. The aging results with this real-life type profile and with an adjusted WLTC (Worldwide Harmonized Light Vehicles Test Cycles) profile are presented and compared. The effectiveness of the use of the WLTC profile for aging tests in real life automotive application is shown.