![]() All these are the issues around the system-level prognostics. Few or no failure data exist in the real operation or by the testbed. Models are rarely available due to the system complexity, which means that the data-driven method may be the only option. Dedicated algorithms may not work in one way or the other in the system. On the contrary, system-level prognostics contains multiple sensors from various components. In addition, a dedicated algorithm can be developed for feature extraction of the target component. Since components are relatively easy to test, a large number of failure data can be obtained from a testbed for the algorithm development. At the component level, a single or a set of sensors, such as vibration, acoustic emission, and temperature sensors, can be used to monitor damage degradation. From the research viewpoint, the system-level prognostics has different characteristics from those of the component-level as summarized in Figure 2. It should be noted that the degradation and health condition of the system is determined by its components, which means that the individual degradation of components should be explored first and integrated to assess the system performance. A complex system is composed of many interlinked components, which makes the system-level prognostics difficult. ![]() Īs the industrial systems in the field become more complex, comprising of multiple components, system-level prognostics is gaining much more interest from industry and academia. However, most of the reviews have focused on the component-level prognostics, such as the bearings, gears, and batteries. All the reviews have provided successful case studies and useful descriptions of prognostics algorithms. presented practical options for prognostics to select an appropriate method for different applications. provided a systematic review of machinery prognostics from the data acquisition to the RUL prediction and summarized several prognostics datasets commonly used for the research. provided a comprehensive review of the PHM followed by an introduction of a systematic PHM design methodology for converting data into prognostic information. To date, there are many valuable review papers and books in the PHM with diverse aspects such as the general process of PHM, pre-processing, and prognostics algorithms. Levels of prognostics and health management. This article focuses on the prognostics of complex systems that are encountered in the real industry. In view of the CBM, however, the prognostics is the most important since it enables the proactive maintenance plan. For example, effective sensor network design for sensing, feature extraction, observability analysis, and diagnostics algorithm for fault diagnostics, development of prognostics algorithm, and proper system operation strategy for health management. Finally, the health management of the system is performed based on the information obtained from diagnostics and prognostics. Prognostics includes establishing a failure precursor which indicates an incipient degradation of the system and estimates the RUL based on the current health state and expected future operating conditions. On the other hand, health prognostics aims to provide information about the future operability of the system. In other words, it focuses on the current operability of the system at stake. Health diagnostics is the process of evaluating the degree of damage significance and identifying the root causes of failure. In the sensing stage, PHM engineers determine what to measure and which kind of sensors to install. ![]() The PHM consists of four main stages: sensing, diagnostics, prognostics, and health management, which are illustrated in Figure 1. The PHM aims to predict the remaining useful life (RUL) of the system and suggest an optimal health management strategy. Prognostics and health management (PHM) has attracted much attention as the enabler of CBM. Condition-based maintenance (CBM) is a maintenance policy that maintains the reliability of system operation and reduces the downtime of the system.
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