•The machinery prognostic program is divided into four technical processes.•The four technical processes are reviewed systematically in order.•Some typical public datasets for prognostics are ...summarized.•The health indicator construction and health stage division processes are reviewed.•This paper gives a comprehensive review through analyzing large amount of references.
Machinery prognostics is one of the major tasks in condition based maintenance (CBM), which aims to predict the remaining useful life (RUL) of machinery based on condition information. A machinery prognostic program generally consists of four technical processes, i.e., data acquisition, health indicator (HI) construction, health stage (HS) division, and RUL prediction. Over recent years, a significant amount of research work has been undertaken in each of the four processes. And much literature has made an excellent overview on the last process, i.e., RUL prediction. However, there has not been a systematic review that covers the four technical processes comprehensively. To fill this gap, this paper provides a review on machinery prognostics following its whole program, i.e., from data acquisition to RUL prediction. First, in data acquisition, several prognostic datasets widely used in academic literature are introduced systematically. Then, commonly used HI construction approaches and metrics are discussed. After that, the HS division process is summarized by introducing its major tasks and existing approaches. Afterwards, the advancements of RUL prediction are reviewed including the popular approaches and metrics. Finally, the paper provides discussions on current situation, upcoming challenges as well as possible future trends for researchers in this field.
Event-based cameras are the emerging bio-inspired technology in vision sensing. Different from the traditional standard cameras, the event-based cameras asynchronously record the brightness change ...per pixel, and have the great merits of high temporal resolution, low energy consumption, high dynamic range, etc . While the event-based cameras have been initially exploited in several common vision-based tasks in the recent years, the investigation on machine condition monitoring problem is quite limited. This paper offers the first attempt in the current literature on exploring the contactless event vision data for machine fault diagnosis. A vibration event representation is proposed to transform the event records into typical data samples, and a deep convolutional neural network model is used for processing the event information. To enhance the model robustness against environmental noisy vision events, an event data augmentation method is proposed to introduce variations of the event patterns. A deep representation clustering method is further proposed to improve the pattern recognition performance with respect to different machine health conditions. Experiments on the event vision-based rotating machine fault diagnosis problem are carried out. It is extensively validated that high fault diagnosis accuracies can be obtained using the vision data from the event-based cameras, which are competitive with the popular accelerometer data. Considering the properties of flexibility, portability and data recognizability, the event-based cameras thus provide a promising new tool for contactless machine health condition monitoring and fault diagnosis.
Le Programme de développement durable à l'horizon 2030 se caractérise par un degré d'ambition sans précédent, mais il représente aussi un formidable défi pour les pays du fait de la complexité et de ...l'imbrication de ses 17 objectifs et 169 cibles. Afin d'aider les gouvernements nationaux à le mettre en oeuvre, l'OCDE a mis au point une méthode unique permettant de comparer les progrès accomplis ainsi que les dynamiques sous-jacentes pour l'ensemble des objectifs et cibles du Programme de Développement Durable.
The condition monitoring of rotating machinery systems based on effective and intelligent fault diagnosis has been widely accepted. Traditional signal processing (SP) methods are less effective due ...to noises and interferences from different sources and incipient faults which remain active for a short time with a particular frequency. In recent times, SP techniques along with artificial intelligence methods are being used for fault classification. Various complex approaches in SP domain have used for feature extraction of the vibration data to design a feature set. A challenging task is to select dominant features from the available feature set for improving the accuracy of fault classification. Thus, motivated by spectral kurtosis (SK) and extreme learning machine (ELM), we propose a novel intelligent diagnosis method for fault classification of rotating machines. In this paper, SK is used as an input feature set to avoid the task of finding the dominant feature set. The extracted features are fed to ELM for fault identification. However, ELM performance primarily depends upon the hidden node parameters and the number of hidden nodes. The selection of optimum ELM parameters for good performance is an open issue. Therefore, modified bidirectional search with local search method is proposed to determine the optimum set of ELM parameters. The developed method is tested on two vibration data sets of rolling element bearings. We examined the significance of SK as a feature set and improved ELM in comparison with traditional methods. The experimental results demonstrate that the proposed method efficiently optimizes the ELM parameters to provide a compact ELM architecture and also enhances the fault classification accuracy.
Machinery Vibration Analysis and Predictive Maintenance provides a detailed examination of the detection, location and diagnosis of faults in rotating and reciprocating machinery using vibration ...analysis. The basics and underlying physics of vibration signals are first examined. The acquisition and processing of signals is then reviewed followed by a discussion of machinery fault diagnosis using vibration analysis. Hereafter the important issue of rectifying faults that have been identified using vibration analysis is covered. The book also covers the other techniques of predictive maintenance such as oil and particle analysis, ultrasound and infrared thermography. The latest approaches and equipment used together with the latest techniques in vibration analysis emerging from current research are also highlighted.
1. Understand the basics of vibration measurement2. Apply vibration analysis for different machinery faults3. Diagnose machinery-related problems with vibration analysis techniques
In the recent years, data-driven machinery fault diagnostic methods have been successfully developed, and the tasks where the training and testing data are from the same distribution have been well ...addressed. However, due to sensor malfunctions, the training and testing data can be collected at different places of machines, resulting in the feature space with significant distribution discrepancy. This challenging issue has received less attention in the current literature, and the existing approaches generally fail in such scenarios. This article proposes a domain adaptation method for machinery fault diagnostics based on deep learning. Adversarial training is introduced for marginal domain fusion, and unsupervised parallel data are explored to achieve conditional distribution alignments with respect to different machine health conditions. Experiments on two rotating machinery datasets are carried out for validations. The results suggest the proposed method is promising to address the fault diagnostic tasks with data from different places of machines, further enhancing applicability of data-driven methods in real industries.
