Novel complete ensemble EMD with adaptive noise-based hybrid filtering for rolling bearing fault diagnosis

A feature extraction of fault bearing has attracted considerable attention in recent years. However, weak fault feature is difficult to extract under heavy background noise. To solve this problem, a novel multi-layer filtering method is proposed to filter out noise and extract weak fault feature. The first layer introduces a metric based on de-trended fluctuation analysis (DFA) to identify intrinsic mode function (IMF) that reflect period impulsive information for vibration signal adaptively. The second layer uses non-local mean (NLM) method as a pre-filter of the third layer to realize extraction of singular value decomposition (SVD) which reflect the most information of IMFs. The last layer introduces a relative energy difference criterion of a singular value to extract important feature of Hankel matrix of IMFs. The filtered signal is obtained by re-constructed signal from identified singular value of SVD. Experiment results on simulation and real vibration signals indicate that the hybrid filtering method removes heavy noise successfully and extract weak fault feature of rolling bearing effectively

Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems

Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industries

A novel bearing multi-fault diagnosis approach based on weighted permutation entropy and an improved SVM ensemble classifier

Timely and accurate state detection and fault diagnosis of rolling element bearings are very critical to ensuring the reliability of rotating machinery. This paper proposes a novel method of rolling bearing fault diagnosis based on a combination of ensemble empirical mode decomposition (EEMD), weighted permutation entropy (WPE) and an improved support vector machine (SVM) ensemble classifier. A hybrid voting (HV) strategy that combines SVM-based classifiers and cloud similarity measurement (CSM) was employed to improve the classification accuracy. First, the WPE value of the bearing vibration signal was calculated to detect the fault. Secondly, if a bearing fault occurred, the vibration signal was decomposed into a set of intrinsic mode functions (IMFs) by EEMD. The WPE values of the first several IMFs were calculated to form the fault feature vectors. Then, the SVM ensemble classifier was composed of binary SVM and the HV strategy to identify the bearing multi-fault types. Finally, the proposed model was fully evaluated by experiments and comparative studies. The results demonstrate that the proposed method can effectively detect bearing faults and maintain a high accuracy rate of fault recognition when a small number of training samples are available

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

Approaches for diagnosis and prognosis of asset condition: application to railway switch systems

This thesis presents a novel fault diagnosis and prognosis methodology which is applied to railway switches. To improve on existing fault diagnosis, energy-based thresholding wavelets (EBTW) are introduced. EBTW are used to decompose sensor measurement signals, and then to reconstruct them within a lower dimensional feature vector. The extracted features replace the original signals and are fed into a neural network classifier for fault diagnosis. Compared to existing wavelet-based feature extraction methods, the new EBTW method has the advantage of an intrinsic energy conservation property during the wavelet transform process. The EBTW method localises and redistributes the signal energy to realise an efficient feature extraction and dimension reduction. The presented diagnosis approach is validated using real-world switch data collected from the Guangzhou Metro in China. The results show that the proposed diagnosis approach can achieve 100% accuracy in identifying a railway switch overdriving fault with various severities, improving upon existing methods of conventional discrete wavelet transform (C-DWT) and soft-thresholding discrete wavelet transform (ST-DWT) by 8.33% and 16.67%, respectively. The presented prognosis approach is constructed based on traditional data-driven prognosis modelling. The concept of a remaining maintenance-free operating period (RMFOP) is introduced, which transforms the usefulness of sensor measurement data that is readily available from operations prior to failure. Useful features are then extracted from the original measurement data, and modelled using linear and exponential regression curve fitting models. By extracting key features, the original measurement data can be transformed into degradation signals that directly reflect the variations in each movement of a switch machine. The features are then fed into regression models to derive the probability distribution of switch residual life. To update the probability distribution from one operation to the next, Bayesian theory is incorporated into the models. The proposed RMFOP-based approach is validated using real-world electrical current sensor measurement data that were collected between January 2018 and February 2019 from multiple operational railway switches across Great Britain. The results show that the linear model and the exponential model can both provide residual life distributions with a satisfactory prediction accuracy. The exponential model demonstrates better predictions, the accuracy of which exceeds 95% when 90% life percentage has elapsed. By applying the RMFOP-based prognosis approach to operational data, the railway switch health condition that is affected by incipient overdriving failure is predicted

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

Research of planetary gear fault diagnosis based on permutation entropy of CEEMDAN and ANFIS

For planetary gear has the characteristics of small volume, light weight and large transmission ratio, it is widely used in high speed and high power mechanical system. Poor working conditions result in frequent failures of planetary gear. A method is proposed for diagnosing faults in planetary gear based on permutation entropy of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Adaptive Neuro-fuzzy Inference System (ANFIS) in this paper. The original signal is decomposed into 6 intrinsic mode functions (IMF) and residual components by CEEMDAN. Since the IMF contains the main characteristic information of planetary gear faults, time complexity of IMFs are reflected by permutation entropies to quantify the fault features. The permutation entropies of each IMF component are defined as the input of ANFIS, and its parameters and membership functions are adaptively adjusted according to training samples. Finally, the fuzzy inference rules are determined, and the optimal ANFIS is obtained. The overall recognition rate of the test sample used for ANFIS is 90%, and the recognition rate of gear with one missing tooth is relatively high. The recognition rates of different fault gears based on the method can also achieve better results. Therefore, the proposed method can be applied to planetary gear fault diagnosis effectively.Transport Engineering and Logistic