A frequency slice wavelet transform based on wavelet de-noising using neighboring coefficients method and its application in feature extraction of rolling bearing’ early weak fault

Extracting the characteristics of rolling bearings in early weak failure stage before the occurring of complete failure has important safety and economic significance in engineering application. The wavelet transform (WT) is the commonly used and effective time-frequency method for fault feature extraction of rotating machinery due to that it could reflect the fault feature in time and frequency domains synchronously. However, WT would not work effectively when the impulsive fault signal is buried by strong background noise, and the situation is particularly serious in the early weak fault stage of rolling bearing. A frequency slice wavelet transform based on wavelet de-noising using neighboring coefficients method is proposed in the paper by combing frequency slice wavelet transform with wavelet de-noising using neighboring coefficient to solve the above problem: Firstly, the vibration signal of rolling bearing is de-noised by wavelet de-noising using the neighboring coefficients method. Then the frequency slice wavelet transform is applied on the de-noised signal, and satisfactory analysis results could be obtained. The effectiveness of the proposed method is verified by the vibration data of rolling bearing accelerated fatigue test. Besides, the analysis result of the same vibration data of rolling bearing accelerated fatigue test using Kurtogram method is also presented in the paper to verify the advantage of the proposed method

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

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

A multi-layer approach to DN 50 electric valve fault diagnosis using shallow-deep intelligent models

Research data for this article: Data not available / Data will be made available on request.Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S1738573320300279#appsec1 .Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.Project for State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment (No.K-A2019.418); Technical Support Project for Suzhou Nuclear Power Research Institute (SNPI, No.029-GN-b-2018-C45-P.0.99–00003); The Basic Research Project (No. JCKY2017xx7B019); Foundation of Science and Technology on Reactor System Design Laboratory (No. HT-KFKT-14-2017003)

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Advances in Bearing Lubrication and Thermal Sciences

This reprint focuses on the hot issue of bearing lubrication and thermal analysis, and brings together many cutting-edge studies, such as bearing multi-body dynamics, bearing tribology, new lubrication and heat dissipation structures, bearing self-lubricating materials, thermal analysis of bearing assembly process, bearing service state prediction, etc. The purpose of this reprint is to explore recent developments in bearing thermal mechanisms and lubrication technology, as well as the impact of bearing operating parameters on their lubrication performance and thermal behavior

The 1993 Goddard Conference on Space Applications of Artificial Intelligence

This publication comprises the papers presented at the 1993 Goddard Conference on Space Applications of Artificial Intelligence held at the NASA/Goddard Space Flight Center, Greenbelt, MD on May 10-13, 1993. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed

Proceedings of the 18th Irish Conference on Artificial Intelligence and Cognitive Science

These proceedings contain the papers that were accepted for publication at AICS-2007, the 18th Annual Conference on Artificial Intelligence and Cognitive Science, which was held in the Technological University Dublin; Dublin, Ireland; on the 29th to the 31st August 2007. AICS is the annual conference of the Artificial Intelligence Association of Ireland (AIAI)

Ubiquitous Technologies for Emotion Recognition

Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now, with the advent of wearable, mobile, and ubiquitous technologies, that we can aim to sense and recognize emotions, continuously and in real time. This book brings together the latest experiences, findings, and developments regarding ubiquitous sensing, modeling, and the recognition of human emotions