Study on fault diagnosis method of splitter intelligent wheelchair motor based on wavelet and energy feature

2021 ◽  
pp. 095745652110004
Author(s):  
Cheng Yang ◽  
Mengfei Zhang ◽  
Bo Zhou
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Main Research ◽  
Rotating Machine ◽  
Intelligent Wheelchair ◽  
The Disabled ◽  
Energy Feature ◽  
Misalignment Fault

As a key component of a split-type intelligent sports wheelchair for the disabled, the reliability of the motor is related to the personal safety of the wheelchair user and the accurate realization of the wheelchair’s sports functions. This motor is actually just a rotating machine. In order to achieve detection and analysis of rotating machinery bearing vibration signal, a method based on wavelet and energy feature of rotating machinery fault diagnosis is introduced. This method applies wavelet to obtain de-noising and then uses wavelet packet energy feature extraction to diagnose faults effectively caused by rotating machinery such as rotor unbalance fault, rotor misalignment fault, and rotor-to-stator rub fault. Test results illustrate that when different faults occur to the bearing, it is viable to utilize pattern recognition to diagnose faults for the reason that discrepancies appear in sub-hand energy after wavelet packet decomposition. The main research conclusions of this paper are also directly applied to the fault diagnosis of such wheelchair motors.

scholarly journals Fault Diagnosis Approach for Rotating Machinery Based on Feature Importance Ranking and Selection

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Zong Yuan ◽  
Taotao Zhou ◽  
Jie Liu ◽  
Changhe Zhang ◽  
Yong Liu
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Ranking And Selection ◽  
Fault Features ◽  
Feature Importance ◽  
Importance Ranking ◽  
Common Signal ◽  
Diagnosis Approach

The key to fault diagnosis of rotating machinery is to extract fault features effectively and select the appropriate classification algorithm. As a common signal decomposition method, the effect of wavelet packet decomposition (WPD) largely depends on the applicability of the wavelet basis function (WBF). In this paper, a novel fault diagnosis approach for rotating machinery based on feature importance ranking and selection is proposed. Firstly, a two-step principle is proposed to select the most suitable WBF for the vibration signal, based on which an optimized WPD (OWPD) method is proposed to decompose the vibration signal and extract the fault information in the frequency domain. Secondly, FE is utilized to extract fault features of the decomposed subsignals of OWPD. Thirdly, the categorical boosting (CatBoost) algorithm is introduced to rank the fault features by a certain strategy, and the optimal feature set is further utilized to identify and diagnose the fault types. A hybrid dataset of bearing and rotor faults and an actual dataset of the one-stage reduction gearbox are utilized for experimental verification. Experimental results indicate that the proposed approach can achieve higher fault diagnosis accuracy using fewer features under complex working conditions.

Analysis and Sensitivity Evaluation of AE Signals and Vibration Signals for Fault Diagnosis of Low-Speed Rotating Machinery

2011 ◽  
Vol 128-129 ◽  
pp. 79-84 ◽  
Author(s):  
Hai Yang Jiang ◽  
Peng Chen
Keyword(s):  
Fault Diagnosis ◽  
Evaluation Method ◽  
Early Stage ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Low Speed ◽  
Rotating Machine ◽  
Sensitivity Evaluation ◽  
Ae Signal ◽  
Ae Signals

The fault detection of low-speed rotating machine is very difficult at the early stage. Because it often breaks down suddenly, there are many problems in the planned maintenance of low-speed rotating machinery in industry plants. In order to detect fault of low-speed rotating machinery as earlier as possible, this paper proposes a sensitivity evaluation method of fault diagnosis by using symptom parameters and frequency analysis of vibration signal and acoustic emission (AE) signal measured for the diagnosis. The practical examples are shown for explaining the efficiency of the sensitivity evaluation method proposed in this paper.

scholarly journals A Lighted Deep Convolutional Neural Network Based Fault Diagnosis of Rotating Machinery

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2381 ◽  
Author(s):  
Shangjun Ma ◽  
Wei Cai ◽  
Wenkai Liu ◽  
Zhaowei Shang ◽  
Geng Liu
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Learning Ability ◽  
Support Vector ◽  
Deep Convolutional Neural Networks ◽  
Convolutional Network ◽  
Memory Space ◽  
Distribution Features

To improve the fault diagnosis performance for rotating machinery, an efficient, noise-resistant end-to-end deep learning (DL) algorithm is proposed based on the advantages of the wavelet packet transform in vibration signal processing (the capability to extract multiscale information and more spectral distribution features) and deep convolutional neural networks (good classification performance, data-driven design and high transfer-learning ability). First, a vibration signal is subjected to pyramid wavelet packet decomposition, and each sub-band coefficient is used as the input for each channel of a deep convolutional network (DCN). Then, based on the lightweight modeling requirements and techniques, a new DCN structure is designed for the fault diagnosis. The proposed algorithm is compared with the support vector machine algorithm and the published DL algorithms based on a bearing dataset produced by Case Western Reserve University. The experimental results show that the proposed algorithm is superior to the existing algorithms in terms of accuracy, memory space, computational complexity, noise resistance, and transfer performance, producing good results.

Study on Mechanical Mechanics with the Application of an Anti-Aliasing Wavelet Packet Algorithm in Fault Diagnosis of Rotating Machine

2013 ◽  
Vol 327 ◽  
pp. 233-236
Author(s):  
Xin Tao Jiao
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Characteristic Frequencies ◽  
Wind Generator ◽  
Rotating Machine ◽  
Gear Box ◽  
Simulation Signal ◽  
Improved Algorithm

Shortcomings inhered in the canonical wavelet packet algorithm are explained in detail. An anti-aliasing wavelet packet algorithm is proposed. The method is used to analyze a typical simulation signal and a vibration signal measured from a gear box in wind generator to study the mechanical mechanics. The results show that the improved algorithm is quite effective in overcoming the shortcomings of the canonical method. It can extract the characteristic frequencies of rotating machine precisely and effectively.

Studies of filtering effect on fault diagnosis of spindle device in hoist

2021 ◽  
Vol 63 (6) ◽  
pp. 348-356
Author(s):  
Jun Gu ◽  
Yuxing Peng ◽  
Bobo Cao
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Correct Diagnosis ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Practical Significance ◽  
Support Vector ◽  
Mode Decomposition ◽  
Core Components ◽  
Filtering Effect

Spindle devices, which are among the core components of mine hoists, are typical rotor-bearing systems. Vibration-based fault diagnosis techniques are often used to help prevent mechanical failures of such systems. The fault vibration signals generally include pulse information reflecting fault type, independent vibration components caused by other non-faulty mechanical components, noise in the surrounding environment and so on. The reduction of noise in the vibration signal collected by the sensor is of practical significance for the correct diagnosis of subsequent rotating machinery faults. To solve this problem, a fault diagnosis method based on a smooth (SM) filtering algorithm combined with variational mode decomposition (VMD) and a support vector machine (SVM) is proposed. Wavelet transform (WT) and wavelet packet transform (WPT) methods are used to compare the noise reduction. The reliability and effectiveness of the method are verified by experiments on a hoist mechanical fault simulator. Experimental results show that the proposed method has high prediction accuracy and can provide a good practical reference for fault diagnosis of rotating machinery.

scholarly journals A fault diagnosis method based on Auxiliary Classifier Generative Adversarial Network for rolling bearing

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0246905
Author(s):  
Chunming Wu ◽  
Zhou Zeng
Keyword(s):  
Fault Diagnosis ◽  
Noise Pollution ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Generative Adversarial Network ◽  
High Background ◽  
Bearing Fault Diagnosis ◽  
Adversarial Network ◽  
Practical Engineering

Rolling bearing fault diagnosis is one of the challenging tasks and hot research topics in the condition monitoring and fault diagnosis of rotating machinery. However, in practical engineering applications, the working conditions of rotating machinery are various, and it is difficult to extract the effective features of early fault due to the vibration signal accompanied by high background noise pollution, and there are only a small number of fault samples for fault diagnosis, which leads to the significant decline of diagnostic performance. In order to solve above problems, by combining Auxiliary Classifier Generative Adversarial Network (ACGAN) and Stacked Denoising Auto Encoder (SDAE), a novel method is proposed for fault diagnosis. Among them, during the process of training the ACGAN-SDAE, the generator and discriminator are alternately optimized through the adversarial learning mechanism, which makes the model have significant diagnostic accuracy and generalization ability. The experimental results show that our proposed ACGAN-SDAE can maintain a high diagnosis accuracy under small fault samples, and have the best adaptation performance across different load domains and better anti-noise performance.

Enhanced deep residual network with multilevel correlation information for fault diagnosis of rotating machinery

2020 ◽  
pp. 107754632094971 ◽  
Author(s):  
Shoucong Xiong ◽  
Shuai He ◽  
Jianping Xuan ◽  
Qi Xia ◽  
Tielin Shi
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Learning Algorithms ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Classification Performance ◽  
Residual Network ◽  
Time Frequency ◽  
Correlation Information

Modern machinery becomes more precious with the advance of science, and fault diagnosis is vital for avoiding economical losses or casualties. Among massive diagnosis methods, deep learning algorithms stand out to open an era of intelligent fault diagnosis. Deep residual networks are the state-of-the-art deep learning models which can continuously improve performance by deepening the network structures. However, in vibration-based fault diagnosis, the transient property instability of vibration signal usually calls for time–frequency analysis methods, and the characters of time–frequency matrices are distinct from standard images, which brings some natural limitations for the diagnosis performance of deep learning algorithms. To handle this issue, an enhanced deep residual network named the multilevel correlation stack-deep residual network is proposed in this article. Wavelet packet transform is used to preprocess the sensor signal, and then the proposed multilevel correlation stack-deep residual network uses kernels with different shapes to fully dig various kinds of useful information from any local regions of the processed input. Experiments on two rolling bearing datasets are carried out. Test results show that the multilevel correlation stack-deep residual network exhibits a more satisfactory classification performance than original deep residual networks and other similar methods, revealing significant potentials for realistic fault diagnosis applications.

Fault Diagnosis of Large Rotors Based on Wavelet Packet Neural Network

2014 ◽  
Vol 722 ◽  
pp. 363-366
Author(s):  
You Juan Zheng ◽  
Ping Liao ◽  
Cai Long Qin ◽  
Yu Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Test Bench ◽  
Feature Vector ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Network Method ◽  
Spectrum Characteristic ◽  
Fault Information

Using wavelet packet neural network method which is consist of wavelet packet and BP neural network to diagnose large rotors by vibration signal .Firstly , according to the spectrum characteristic of large rotors’ common vibration fault ,using the improved wavelet packet method to compute the energy of the spectrum that can reflect the fault information .And then make the feature vector as the input to establish a model of improved wavelet packet neural network for fault diagnosis . Collect the data of five working conditions from the test bench , establish a improved wavelet packet neural network model, and then use the model to diagnose fault. The experimental results show that this method improves the accuracy obviously and calculate fast.

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