Several decades ago, the prevalent concept of the community was that there were fundamentally different approaches to diagnosing and maintaining mechanical versus electrical/electronic devices. For mechanical devices, the approaches were more concentrated on wear and tear, based on life-based model estimation; for electrical/electronic devices, the approaches were concentrated only on probabilistic and random phenomena. In other words, electronic approaches were mainly confined to statistics, assigning a probabilistic value of failure to each of the components to determine the overall reliability of the equipment. With some few exceptions, while the statistical approach is extremely valuable, we can do more in understanding reliability because the process of electromagnetic energy conversion of a device requires matter. More specifically, matter is the enabler of the process, channeling and regulating the conversion of one form of energy to another, usually from electric to magnetic or vice versa. The fact that the converters require matter as “the enabler” exposes the fundamental principle: that aging governs electrical elements in the same fashion as do the mechanical parts. Consequently, solid-state materials such as conductors, insulators, or semiconductors transfer energy from molecule to molecule, atom to atom, degrading during the process, which is modified by the level of temperature, electromagnetic fields, humidity, and other factors. Therefore, small cracks that appear due to impurities or “hot collisions” progress over time and are manifested as aging in the material, which usually shows as a loss of elasticity or an increase in the losses associated with energy transfer. This process creates a degradation “marker” that can be identified in many cases at the early stages of the degradation process. The understanding, identification, and progression of these degradation markers are the focal point of this chapter.

This chapter presents an RUL prediction algorithm based on accelerated life test data and derived physics models for electrolytic capacitors. The main elements are (1) development of the first principles based degradation models; (2) the implementation of a Bayesian-based health state tracking and RUL prediction algorithm based on the Kalman filtering framework. One major advancement reported here is the prediction of RUL for capacitors as new measurements become available. The key contribution of this work is the prediction of RUL for capacitors as new measurements become available. The derived degradation models can be updated and developed at a finer granularity to be implemented for detailed prognostic implementation. This capability increases the technology readiness level ofprognostics applied to electrolytic capacitors.The results presented here are based on accelerated life test data and on the accelerated life timescale. Further research will focus on development of functional mappings that will translate the accelerated life timescale into real usage conditions timescale, where the degradation process dynamics will be slower and subject to several types of stresses. The performance of the proposed exponential-based degradation model is satisfactorily based on the quality of the model fit to the experimental data and the RUL prediction performance as compared to ground truth.

The widespread use of power electronics-based converters has resulted in continuous improvement in the power switches' reliability. The previous authors shows a failure rate of medium-power IGBT modules of 0.1 failures per 10⁶ h, many critical applications need further assurance of operation. This chapter has shown a sample of the techniques used to increase the system operating range under adverse conditions. Some techniques are applied before the onset of a device's fault, by allowing the device to move out of line for “recovery.” Other techniques modify the topology, either by disabling the faulty devices or by also including extra elements to replace the faulty ones. In most cases, this operation needs a corresponding change in the control strategy, as well as in the amount of power that the system can handle during this fault-tolerant operation.

This chapter focuses on lithium-ion batteries. It also covers relevant aspects of this technology, focusing on the impedance measurement as a diagnostic tool mechanism for detecting aging on this type of batteries. Furthermore, we divide such mechanisms into online and offline methods based on their means of operation during the service time of the battery.