This paper presents a MATLAB-based tutorial to conduct fault diagnosis of a rolling element bearing. While there have been so many new developments in this field, no studies have addressed the tutorial aspects in this field to help the engineers learn the concept and implement by their own effort. The three most common techniques—the autoregressive model, spectral kurtosis, and envelope analysis—are selected to demonstrate the bearing diagnosis process. Simulation signal is introduced to help understand the characteristics of fault signal and carry out the process toward the fault identification. The techniques are then applied to the two real datasets to demonstrate the practical applications, one made by the authors and the other by the Case Western Reserve University, which is known as a standard reference in testing the diagnostic algorithms.

In the gear fault diagnostics, conventional methods have largely depended on the complicated signal processing and feature extraction skills, which are often cumbersome for engineers to implement easily. Recently, the convolutional neural networks, which is a kind of deep learning techniques, have found increased success in this field by taking advantage of minimal engagement of signal processing and automated features extraction for the fault diagnosis. In the previous studies, however, the accuracy of the method was often assessed by means of cross-validation from the data sets at the same faulted tooth, which may not be the case in the real gear, since the fault may appear at different teeth from the training. This article proposes a convolutional neural network method based on the signal segmentation to solve this problem, which is to divide the original signal into those at each tooth of the gear. The effectiveness of the method is validated by the data made from the gearbox test rig, in which the vibration and transmission errors are measured, respectively. The training and test data sets are prepared at different fault locations. The performances of the convolutional neural network with signal segmentation are compared and discussed with those by the ordinary convolutional neural network without segmentation. As a whole, the results with the signal segmentation suggest that the fault can be successfully identified even when the fault location in the test is different from that of the training, which provides great feasibility toward the real applications.

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.

Prognostics and health management (PHM) has become an essential function for safe system operation and scheduling economic maintenance. To date, there has been much research and publications on component-level prognostics. In practice, however, most industrial systems consist of multiple components that are interlinked. This paper aims to provide a review of approaches for system-level prognostics. To achieve this goal, the approaches are grouped into four categories: health index-based, component RUL-based, influenced component-based, and multiple failure mode-based prognostics. Issues of each approach are presented in terms of the target systems and employed algorithms. Two examples of PHM datasets are used to demonstrate how the system-level prognostics should be conducted. Challenges for practical system-level prognostics are also addressed.

Gears and bearings are one of the major components of many machines, which can result in operation downtime or even catastrophic failure of a whole system. This paper addresses a tutorial for the features extraction and selection of the gears and bearings, which is known as feature engineering, a prerequisite step for the prognostics and health management (PHM) of these components. While there have been many new developments in this field, no studies have addressed the tutorial aspects of features engineering to aid engineers in solving problems by their own effort, which is of practical importance for successful PHM. The paper aims at helping beginners learn the basic concepts, and implement the algorithms using the public datasets as well as those made by the authors. Matlab codes are provided for them to implement the process by their own hands.