rolling element bearings fault intelligent diagnosis

2020-3-1A robust intelligent fault diagnosis method for rolling element bearings based on deep distance metric learning[J] Neurocomputing 2018 310: 77-95 Guo L Lei Y Xing S et al Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data [J] 2019-12-25fault diagnosis based on stacked auto-encoder and softmax regression In [17]–[19] authors have proposed a DNN-based intelligent fault diagnosis method for the classification of different datasets from bearings element and gearboxes with large samples using auto-encoder Sun et al [20] proposed a

A Fault Diagnosis Method of Rolling Bearing Based on

In view of some shortcomings of traditional rolling bearing fault diagnosis for instance feature extraction relies heavily on subjective experience of people and the extracted features do not have high recognition rate for rolling element faults a new fault type intelligent diagnosis method transforming signal recognition into image recognition based on time frequency diagram and

2016-11-22Zhang W Peng G Li C (2017) Rolling Element Bearings Fault Intelligent Diagnosis Based on Convolutional Neural Networks Using Raw Sensing Signal In: Pan JS Tsai PW Huang HC (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing Smart Innovation Systems and Technologies vol 64

Yang Junyan Zhang Youyun Zhu Yongsheng and Wang Qinghua Intelligent Fault Diagnosis of Rolling Element Bearing Based on SVMs and Statistical Characteristics Proceedings of the ASME 2007 International Manufacturing Science and Engineering Conference ASME 2007 International Manufacturing Science and Engineering Conference Atlanta

2017-12-18To employ CNN to resolve the problem of rolling-element bearings fault diagnosis in the present work the raw 1-D AE signal is transformed into a 2-D kurtogram representation Experimental results using eight types of various bearing conditions indicate that the proposed fault diagnosis approach utilizing the kurtogram representation of the

This paper presents a new method for fault diagnosis of rolling element bearings which is developed based on a combination of weighted K nearest neighbor (W K NN) classifiers This method uses wavelet packet transform based on the lifting scheme to preprocess the vibration signals before feature extraction

Lightweight Convolutional Neural Network and Its

2019-11-6intelligent fault diagnosis method for rolling element bearings based on the deep distance metric learning [19] The above methods prove that deep learning has high precision and anti-noise in bearing fault diagnosis Although CNN performed well in the experiment it has always faced problems in fault diagnosis:

Early fault diagnosis of rolling element bearing is still a difficult problem Firstly in order to effectively extract the fault impulse signal of the bearing a new enhanced morphological difference operator (EMDO) is constructed by combining two optimal feature extraction-type operators Next in the process of processing the test signal in order to reduce the interference problem caused

Therefore cross-domain intelligent fault detection and diagnosis of bearings is very critical for the reliable operation In this paper a new intelligent fault diagnosis approach based on tensor-aligned invariant subspace learning and two-dimensional convolutional neural networks (TAISL-2DCNN) is proposed for cross-domain intelligent fault

2020-2-10fault is the most common fault type and is responsible for 30% to 40% of all the machine failures The structure of a rolling-element bearing is illustrated in Fig 1 which contains the outer race typically mounted on the motor cap the inner race to hold the motor shaft the balls or the rolling elements and the cage for restraining the relative

2018-5-10Traditional intelligent fault diagnosis methods for rolling bearings heavily depend on manual feature extraction and feature selection For this purpose an intelligent deep learning method named the improved deep recurrent neural network (DRNN) is proposed in this paper

CHEN Xia (School of Mechanical and Electronic Engin WUT Wuhan 430070 China) Research on Intelligent Fault Diagnosis System of Rolling Bearings[A] [C] 2007 8 Chen Xuefeng Li Bing Liu Yunsheng He Zhengjia (State Key Lab for Manufacturing System Engineering Xi'an Jiaotong University Xi'an 710049) CLEARANCES IDENTIFICATION OF GUNS MECHANISM BASED ON THE SECOND

Fault Detection and Diagnosis on the Rolling Element Bearing by Aida Rezaei A thesis submitted to The faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Applied Science Department of Mechanical and Aerospace Engineering Ottawa-Carleton Institute for Mechanical and Aerospace Engineering

Early fault diagnosis of rolling element bearing is still a difficult problem Firstly in order to effectively extract the fault impulse signal of the bearing a new enhanced morphological difference operator (EMDO) is constructed by combining two optimal feature extraction-type operators Next in the process of processing the test signal in order to reduce the interference problem caused

Rolling Bearing Diagnosis Based on Adaptive

Early fault diagnosis of rolling element bearing is still a difficult problem Firstly in order to effectively extract the fault impulse signal of the bearing a new enhanced morphological difference operator (EMDO) is constructed by combining two optimal feature extraction-type operators Next in the process of processing the test signal in order to reduce the interference problem caused

2 This approach can also be augmented with the introduction of more sophisticated rules that trigger alarms based on historical data patterns and anomalous behaviours (i e pattern recognition) in this case a predictive element is introduced in this strategy although it is generally not used to evaluate the residual useful life of a component

(2013) Fault Diagnosis of Rolling Bearings Based on IMF Envelope Sample Entropy and Support Vector Machine (1984) Model for the vibration produced by a single point defect in a rolling element bearing (2014) Reliable fault diagnosis method using ensemble

CiteSeerX - Document Details (Isaac Councill Lee Giles Pradeep Teregowda): Purpose – To present a new application of Pursuit based analysis for diagnosing rolling element bearing faults Methodology- Intelligent diagnosis of rolling element bearing faults in rotating machinery involves the procedure of feature extraction using modern signal processing techniques and artificial intelligence

2019-2-4Fault diagnosis of rolling element bearings using vibration signature analysis is the most commonly used to prevent breakdowns in machinery To analyze vibration signals several methods in different domains have been implemented such as time domain frequency domain and time-

Therefore cross-domain intelligent fault detection and diagnosis of bearings is very critical for the reliable operation In this paper a new intelligent fault diagnosis approach based on tensor-aligned invariant subspace learning and two-dimensional convolutional neural networks (TAISL-2DCNN) is proposed for cross-domain intelligent fault

2019-12-10The diagnosis results prove that the method based on CEEMDAN may reveal the fault characteristic information of rolling element bearings better Keywords Complete ensemble empirical mode decomposition with adaptive noise residual noise fault diagnosis rolling element bearings