Identification of initial fault time for bearing based on monitoring indicator, WEMD and Infogram

Rolling element bearing is a core component in the rotating machine. The performance of the whole machine is mainly dominated by the performance condition of the rolling element bearing. The Initial Fault Time (IFT) is a beginning landmark of the unhealthy condition of bearings. In order to identify accurately and rapidly the IFT under the weak fault signatures and heavy background noise, an identification method of the IFT is proposed by the monitoring indicator and envelope analysis with Weighted Empirical Mode Decomposition (WEMD) and Infogram. The monitoring indicator is constructed by the variation coefficient of the summation of the multiple standardized statistical features of the vibration signal. The approximate IFT can be obtained by the minimum before the early stage of the continuous increase in the monitoring indicator. Whereafter, a more accurate IFT can be detected by envelope analysis with WEMD and Infogram based on interval-halving backtracking strategy. The proposed method is verified by the tested dataset provided by Intelligent Maintenance System (IMS). The results show that the proposed method is efficient, rapid and simple for identifying the IFT

Condition Monitoring and Fault Diagnosis of Roller Element Bearing

Rolling element bearings play a crucial role in determining the overall health condition of a rotating machine. An effective condition-monitoring program on bearing operation can improve a machine’s operation efficiency, reduce the maintenance/replacement cost, and prolong the useful lifespan of a machine. This chapter presents a general overview of various condition-monitoring and fault diagnosis techniques for rolling element bearings in the current practice and discusses the pros and cons of each technique. The techniques introduced in the chapter include data acquisition techniques, major parameters used for bearing condition monitoring, signal analysis techniques, and bearing fault diagnosis techniques using either statistical features or artificial intelligent tools. Several case studies are also presented in the chapter to exemplify the application of these techniques in the data analysis as well as bearing fault diagnosis and pattern recognition

Information Theory and Its Application in Machine Condition Monitoring

Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries

Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression

This reprint focuses on wear and fatigue analysis, the dynamic properties of coating surfaces in transmission systems, and non-destructive condition monitoring for the health management of transmission systems. Transmission systems play a vital role in various types of industrial structure, including wind turbines, vehicles, mining and material-handling equipment, offshore vessels, and aircrafts. Surface wear is an inevitable phenomenon during the service life of transmission systems (such as on gearboxes, bearings, and shafts), and wear propagation can reduce the durability of the contact coating surface. As a result, the performance of the transmission system can degrade significantly, which can cause sudden shutdown of the whole system and lead to unexpected economic loss and accidents. Therefore, to ensure adequate health management of the transmission system, it is necessary to investigate the friction, vibration, and dynamic properties of its contact coating surface and monitor its operating conditions

A study on helicopter main gearbox planetary bearing fault diagnosis

The condition monitoring of helicopter main gearbox (MGB) is crucial for operation safety, flight airworthiness and maintenance scheduling. Currently, the helicopter health and usage monitoring system, HUMS, is installed on helicopters to monitor the health state of their transmission systems and predict remaining useful life of key helicopter components. However, recent helicopter accidents related to MGB failures indicate that HUMS is not sensitive and accurate enough to diagnose MGB planetary bearing defects. To contribute in improving the diagnostic capability of HUMS, diagnosis of a MGB planetary bearing with seeded defect was investigated in this study. A commercial SA330 MGB was adopted for the seeded defect tests. Two test cases are demonstrated in this paper: the MGB at 16,000 rpm input speed with 180 kW load and at 23,000 rpm input speed with 1760 kW load. Vibration data was recorded, and processed using signal processing techniques including self-adaptive noise cancellation (SANC), kurtogram and envelope analysis. Processing results indicate that the seeded planetary bearing defect was successfully detected in both test cases

A Literature Review of Fault Diagnosis Based on Ensemble Learning

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance