Prognostics and systems health management (PHM) is an enabling discipline that uses sensors to assess the health of systems, diagnoses anomalous behavior, and predicts the remaining useful performance over the life of the asset. The advent of the Internet of Things (IoT) enables PHM to be applied to all types of assets across all sectors, thereby creating a paradigm shift that is opening up significant new business opportunities. This paper introduces the concepts of PHM and discusses the opportunities provided by the IoT. Developments are illustrated with examples of innovations from manufacturing, consumer products, and infrastructure. From this review, a number of challenges that result from the rapid adoption of IoT-based PHM are identified. These include appropriate analytics, security, IoT platforms, sensor energy harvesting, IoT business models, and licensing approaches.

Lithium-ion (Li-ion) batteries are used in various applications as the rechargeable power sources. The batteries undergo capacity fade during the repeated charge-discharge cycles, which eventually leads to the end of life (EOL). For the purpose of timely replacement before reaching the EOL, reliable prediction of the remaining useful life (RUL) during the cycles is of great importance. However, there may exist unhealthy batteries exhibiting the change of state at some cycles from those of normal degradation, which leads to their EOL sooner than expected. In this article, we propose a novel prognostic method using the particle filter (PF) that is capable of detecting the point of state change and adapting its algorithm to the new battery degradation pattern. The performance of the proposed method is demonstrated by the case study of Li-ion battery degradation data, comparing with the original PF algorithm. As a result, the proposed method shows better performance in terms of anomaly detection of degradation and adaptability to the new degradation process, which leads to more accurate and reliable RUL prediction.