Incipient defect identification in rolling bearings using adaptive lifting scheme packet

Defects on the surface of rolling bearing elements are some of the most frequent causes of malfunctions and breakages of rotating machines. Defect detection in rolling bearings via techniques that examine changes in measured signal is a very important topic of research due to increasing demands for quality and reliability. In this paper, incipient defect identification method based on adaptive lifting scheme packet is proposed. Adaptive lifting scheme packet operators which adapt to the signal characteristic are constructed. The shock pulse value in defect sensitive frequency band is used as the defect indicator to identify the defect location and severity of rolling bearing. The proposed defect identification method is applied to analyze the experimental signal from rolling bearing with incipient inner raceway defect. The result confirms that the proposed method is accurate and robust in rolling bearing incipient defect identification

Roller Bearing Fault Diagnosis Based on Nonlinear Redundant Lifting Wavelet Packet Analysis

A nonlinear redundant lifting wavelet packet algorithm was put forward in this study. For the node signals to be decomposed in different layers, predicting operators and updating operators with different orders of vanishing moments were chosen to take norm lp of the scale coefficient and wavelet coefficient acquired from decomposition, the predicting operator and updating operator corresponding to the minimal norm value were used as the optimal operators to match the information characteristics of a node. With the problems of frequency alias and band interlacing in the analysis of redundant lifting wavelet packet being investigated, an improved algorithm for decomposition and node single-branch reconstruction was put forward. The normalized energy of the bottommost decomposition node coefficient was calculated, and the node signals with the maximal energy were extracted for demodulation. The roller bearing faults were detected successfully with the improved analysis on nonlinear redundant lifting wavelet packet being applied to the fault diagnosis of the roller bearings of the finishing mills in a plant. This application proved the validity and practicality of this method

Study and Application of Acoustic Emission Testing in Fault Diagnosis of Low-Speed Heavy-Duty Gears

Most present studies on the acoustic emission signals of rotating machinery are experiment-oriented, while few of them involve on-spot applications. In this study, a method of redundant second generation wavelet transform based on the principle of interpolated subdivision was developed. With this method, subdivision was not needed during the decomposition. The lengths of approximation signals and detail signals were the same as those of original ones, so the data volume was twice that of original signals; besides, the data redundancy characteristic also guaranteed the excellent analysis effect of the method. The analysis of the acoustic emission data from the faults of on-spot low-speed heavy-duty gears validated the redundant second generation wavelet transform in the processing and denoising of acoustic emission signals. Furthermore, the analysis illustrated that the acoustic emission testing could be used in the fault diagnosis of on-spot low-speed heavy-duty gears and could be a significant supplement to vibration testing diagnosis

Multi-fault diagnosis for rolling element bearings based on intrinsic mode function screening and optimized least squares support vector machine

Multi-fault diagnosis of rolling element bearing is significant to avoid serious accidents and huge economic losses effectively. However, due to the vibration signal with the character of nonstationarity and nonlinearity, the detection, extraction and classification of the fault feature turn into a challenging task. This paper presents a novel method based on redundant second generation wavelet packet transform (RSGWPT), ensemble empirical mode decomposition (EEMD) and optimized least squares support vector machine (LSSVM) for fault diagnosis of rolling element bearings. Firstly, this method implements an analysis combining RSGWPT-EEMD to extract the crucial characteristics from the measured signal to identify the running state of rolling element bearings, the vibration signal is adaptively decomposed into a number of modified intrinsic mode functions (modified IMFs) by two step screening processes based on the energy ratio; secondly, the matrix is formed by different level modified IMFs and singular value decomposition (SVD) is used to decompose the matrix to obtain singular value as eigenvector; finally, singular values are input to LSSVM optimized by particle swarm optimization (PSO) in the feature space to specify the fault type. The effectiveness of the proposed multi-fault diagnosis technique is demonstrated by applying it to both simulated signals and practical bearing vibration signals under different conditions. The results show that the proposed method is effective for the condition monitoring and fault diagnosis of rolling element bearings

Short-term trend prediction of bearing based on SGWT and MLE method

Bearings are widely used in rotating machines, and the bearing abnormities have significant effects on the operation of machines. Short-term trend prediction of the bearing is very important for the maintenance decision making. In this paper, a novel bearing short-term prediction method based on SGWT and MLE method is developed. The original data is denoised using SGWT, and the maximum Lyapunov exponent which contains bearing fault characteristic information are calculated, and these characteristic parameters are used to predict the short-term trend of bearing. The effectiveness of this methodology is demonstrated using experimental data

Analysis of acoustic emission in the processes of machining using wavelet packets

В даній роботі були проведені дослідження акустичної емісії (АЕ) в рамках моніторингу процесів механічної обробки. АЕ безпосередньо пов’язана зі станом інструменту, так як акустична хвиля генерується за рахунок пластичної деформації, стирання, руйнування та поширення тріщин. У дослідженні, опублікованому в даній роботі було виміряно декілька параметрів АЕ під час механообробки. В той час як ці параметри вказують на ступінь зносуріжучого інструменту, їх інформативність для промислового використання обмежена. Для набуття більшої інформативності була використана теорема вейвлет-пакетів (ВП), за допомогою якої ми ввели додаткові коефіцієнти ентропію та силу. Отримали адекватну модель для визначення стану інструменту.The problem of safe operation and efficient operation of complex technical systems and equipment hazardous industries. A significant depreciation of industrial equipment involves the search for new approaches to solving the problems that stand before the technical diagnosis. The aim was to develop a system of analysis of acoustic emission (AE) for machining processes. Originally performed by analysis of informative parameters of AE. After the experiment, it was found that their information content is insufficient for industrial use. To solve this problem, we used wavelet packet method. Using advanced options and forces of entropy, solved the problem, which was confirmed experimentally. Parameter energy in conjunction with the parameters of AE is a measure of tool wear. When we register peak signals a change in the behavior of other parameters, leading to the rejection of the system. Informative parameters AE can establish dependency on energy consumption equipment that gives a fairly accurate results. The downside can be assumed that all of these parameters and their combination is sensitive to the conditions and needs careful care facilities

NASA Tech Briefs Index, 1977, volume 2, numbers 1-4

Announcements of new technology derived from the research and development activities of NASA are presented. Abstracts, and indexes for subject, personal author, originating center, and Tech Brief number are presented for 1977

Intelligent Bearing Fault Diagnosis Method Combining Mixed Input and Hybrid CNN-MLP model

Rolling bearings are one of the most widely used bearings in industrial machines. Deterioration in the condition of rolling bearings can result in the total failure of rotating machinery. AI-based methods are widely applied in the diagnosis of rolling bearings. Hybrid NN-based methods have been shown to achieve the best diagnosis results. Typically, raw data is generated from accelerometers mounted on the machine housing. However, the diagnostic utility of each signal is highly dependent on the location of the corresponding accelerometer. This paper proposes a novel hybrid CNN-MLP model-based diagnostic method which combines mixed input to perform rolling bearing diagnostics. The method successfully detects and localizes bearing defects using acceleration data from a shaft-mounted wireless acceleration sensor. The experimental results show that the hybrid model is superior to the CNN and MLP models operating separately, and can deliver a high detection accuracy of 99,6% for the bearing faults compared to 98% for CNN and 81% for MLP models

Index to 1985 NASA Tech Briefs, volume 10, numbers 1-4

Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1985 Tech Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences