Fault Diagnosis: Spectral Analysis of the Vibration Signals in Transfer Press

Fault diagnosis using vibration signals is a major challenge for industrial manufacturing. Obtaining defect information is an important step to make decisions about the maintenance in prognostic and health management systems. Existing studies mostly considers vibration signals collected from elements such as rolling element bearings and hydraulic presses. In this paper, we use the vibration signals obtained from the mechanical transfer press during metal forming process and analyze them from the signal processing point of view. Experimental results reveals that spectral analysis is a good candidate for fault diagnosis and it provides important information about the localized faults embedded in the vibration signals.

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Fault Diagnosis for Rotating Machinery Based on Convolutional Neural Network and Empirical Mode Decomposition

Shock and Vibration ◽

10.1155/2017/3084197 ◽

2017 ◽

Vol 2017 ◽

pp. 1-12 ◽

Cited By ~ 17

Author(s):

Yuan Xie ◽

Tao Zhang

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Empirical Mode Decomposition ◽

Spatial Information ◽

Health Management ◽

Rotating Machinery ◽

Intrinsic Mode Functions ◽

Vibration Signals ◽

Mode Decomposition

The analysis of vibration signals has been a very important technique for fault diagnosis and health management of rotating machinery. Classic fault diagnosis methods are mainly based on traditional signal features such as mean value, standard derivation, and kurtosis. Signals still contain abundant information which we did not fully take advantage of. In this paper, a new approach is proposed for rotating machinery fault diagnosis with feature extraction algorithm based on empirical mode decomposition (EMD) and convolutional neural network (CNN) techniques. The fundamental purpose of our newly proposed approach is to extract distinguishing features. Frequency spectrum of the signal obtained through fast Fourier transform process is trained in a designed CNN structure to extract compressed features with spatial information. To solve the nonstationary characteristic, we also apply EMD technique to the original vibration signals. EMD energy entropy is calculated using the first few intrinsic mode functions (IMFs) which contain more energy. With features extracted from both methods combined, classification models are trained for diagnosis. We carried out experiments with vibration data of 52 different categories under different machine conditions to test the validity of the approach, and the results indicate it is more accurate and reliable than previous approaches.

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Higher Order Spectral Analysis of Vibration Signals and Convolutional Neural Network for the Fault Diagnosis of an Induction Motor Bearings

Advances in Intelligent Systems and Computing - Computational Intelligence in Information Systems ◽

10.1007/978-3-030-03302-6_1 ◽

2018 ◽

pp. 3-12

Author(s):

Muhammad Sohaib ◽

Jong-Myon Kim

Keyword(s):

Neural Network ◽

Spectral Analysis ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Induction Motor ◽

Higher Order ◽

Vibration Signals

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COMPOUND FAULT DIAGNOSIS OF BEARINGS USING AN IMPROVED SPECTRAL KURTOSIS BY MAXIMUM CORRELATION KURTOSIS DECONVOLUTION (MCKD).

June-2020 - International Journal of Engineering Sciences & Research Technology ◽

10.29121/ijesrt.v9.i8.2020.8 ◽

2020 ◽

Vol 9 (8) ◽

pp. 66-72

Keyword(s):

Fault Diagnosis ◽

Resonance Frequency ◽

Background Noise ◽

Maximum Correlation ◽

Vibration Signals ◽

Filter Length ◽

Frequency Bands ◽

Noise Interference ◽

Spectral Kurtosis

This paper discusses the use of Maximum Correlation kurtosis deconvolution (MCKD) method as a pre-processor in fast spectral kurtosis (FSK) method in order to find the compound fault characteristics of the bearing, by enhancing the vibration signals. FSK only extracts the resonance bands which have maximum kurtosis value, but sometimes it might possible that faults occur in the resonance bands which has low kurtosis value, also the faulty signals missed due to noise interference. In order to overcome these limitations FSK used with MCKD, MCKD extracts various faults present in different resonance frequency bands; also detect the weak impact component, as MCKD also dealt with strong background noise. By obtaining the MCKD parameters like, filter length & deconvolution period, we can extract the compound fault feature characteristics.

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Core Criteria I

10.1093/oso/9780198793670.003.0004 ◽

2018 ◽

Author(s):

Sanford C. Goldberg

Keyword(s):

Cognitive Processes ◽

Point Of View ◽

Infinite Regress ◽

Belief Formation ◽

Forming Process ◽

Epistemic Criteria ◽

Problem Of The Criterion

Chapter 3 deals with the first issue one faces in the task of articulating the explicit epistemic criteria for belief: the problem of the criterion. It is tempting to suppose that a belief can be normatively proper from the epistemic point of view only if the believer can certify for herself the reliability of every belief-forming process on which she relied. But insisting on this quickly leads to the threat of an infinite regress. This chapter defends a foundationalist response to this problem, according to which we enjoy a default (albeit defeasible) permission to rely on certain cognitive processes in belief-formation. These are processes that satisfy what the author calls the Reliabilist Rationale. Importantly, our permissions here are social: any one of us is permitted to rely on any token process that satisfies this rationale, whether the token process resides in one’s own mind/brain or that of another epistemic subject.

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A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods

Sensors ◽

10.3390/s21082599 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2599

Author(s):

Zhenbao Li ◽

Wanlu Jiang ◽

Sheng Zhang ◽

Yu Sun ◽

Shuqing Zhang

Keyword(s):

Fault Diagnosis ◽

Extreme Learning Machine ◽

Recognition Accuracy ◽

Ensemble Empirical Mode Decomposition ◽

Hydraulic Pump ◽

Vibration Signals ◽

Mode Decomposition ◽

Diagnosis Method ◽

Kernel Extreme Learning Machine ◽

Learning Machine

To address the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an integrated hydraulic pump fault diagnosis method based on the modified ensemble empirical mode decomposition (MEEMD), autoregressive (AR) spectrum energy, and wavelet kernel extreme learning machine (WKELM) methods is presented in this paper. First, the non-linear and non-stationary hydraulic pump vibration signals are decomposed into several intrinsic mode function (IMF) components by the MEEMD method. Next, AR spectrum analysis is performed for each IMF component, in order to extract the AR spectrum energy of each component as fault characteristics. Then, a hydraulic pump fault diagnosis model based on WKELM is built, in order to extract the features and diagnose faults of hydraulic pump vibration signals, for which the recognition accuracy reached 100%. Finally, the fault diagnosis effect of the hydraulic pump fault diagnosis method proposed in this paper is compared with BP neural network, support vector machine (SVM), and extreme learning machine (ELM) methods. The hydraulic pump fault diagnosis method presented in this paper can diagnose faults of single slipper wear, single slipper loosing and center spring wear type with 100% accuracy, and the fault diagnosis time is only 0.002 s. The results demonstrate that the integrated hydraulic pump fault diagnosis method based on MEEMD, AR spectrum, and WKELM methods has higher fault recognition accuracy and faster speed than existing alternatives.

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Optimal multi-kernel local fisher discriminant analysis for feature dimensionality reduction and fault diagnosis

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x211009335 ◽

2021 ◽

pp. 1748006X2110093

Author(s):

Qing Zhang ◽

Heng Li ◽

Xiaolong Zhang ◽

Haifeng Wang

Keyword(s):

Fault Diagnosis ◽

Discriminant Analysis ◽

Feature Space ◽

High Dimensional ◽

Fisher Discriminant Analysis ◽

Vibration Signals ◽

Fisher Discriminant ◽

Local Fisher Discriminant Analysis ◽

Diagnosis Accuracy ◽

Diagnosis Model

To achieve a more desirable fault diagnosis accuracy by applying multi-domain features of vibration signals, it is significative and challenging to refine the most representative and intrinsic feature components from the original high dimensional feature space. A novel dimensionality reduction method for fault diagnosis is proposed based on local Fisher discriminant analysis (LFDA) which takes both label information and local geometric structure of the high dimensional features into consideration. Multi-kernel trick is introduced into the LFDA to improve its performance in dealing with the nonlinearity of mapping high dimensional feature space into a lower one. To obtain an optimal diagnosis accuracy by the reduced features of low dimensionality, binary particle swarm optimization (BPSO) algorithm is utilized to search for the most appropriate parameters of kernels and K-nearest neighbor (kNN) recognition model. Samples with labels are used to train the optimal multi-kernel LFDA and kNN (OMKLFDA-kNN) fault diagnosis model to obtain the optimal transformation matrix. Consequently, the trained fault diagnosis model implements the recognition of machinery health condition with the most representative feature space of vibration signals. A bearing fault diagnosis experiment is conducted to verify the effectiveness of proposed diagnostic approach. Performance comparison with some other methods are investigated, and the improvement for fault diagnosis of the proposed method are confirmed in different aspects.

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A Fusion Feature Extraction Method Using EEMD and Correlation Coefficient Analysis for Bearing Fault Diagnosis

Applied Sciences ◽

10.3390/app8091621 ◽

2018 ◽

Vol 8 (9) ◽

pp. 1621 ◽

Cited By ~ 11

Author(s):

Fan Jiang ◽

Zhencai Zhu ◽

Wei Li ◽

Yong Ren ◽

Gongbo Zhou ◽

...

Keyword(s):

Fault Diagnosis ◽

Correlation Coefficient ◽

Feature Space ◽

Support Vector ◽

Vibration Signals ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Acceleration Sensors ◽

Reconstructed Signal ◽

Original Feature

Acceleration sensors are frequently applied to collect vibration signals for bearing fault diagnosis. To fully use these vibration signals of multi-sensors, this paper proposes a new approach to fuse multi-sensor information for bearing fault diagnosis by using ensemble empirical mode decomposition (EEMD), correlation coefficient analysis, and support vector machine (SVM). First, EEMD is applied to decompose the vibration signal into a set of intrinsic mode functions (IMFs), and a correlation coefficient ratio factor (CCRF) is defined to select sensitive IMFs to reconstruct new vibration signals for further feature fusion analysis. Second, an original feature space is constructed from the reconstructed signal. Afterwards, weights are assigned by correlation coefficients among the vibration signals of the considered multi-sensors, and the so-called fused features are extracted by the obtained weights and original feature space. Finally, a trained SVM is employed as the classifier for bearing fault diagnosis. The diagnosis results of the original vibration signals, the first IMF, the proposed reconstruction signal, and the proposed method are 73.33%, 74.17%, 95.83% and 100%, respectively. Therefore, the experiments show that the proposed method has the highest diagnostic accuracy, and it can be regarded as a new way to improve diagnosis results for bearings.

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