Special Issue "Fault Diagnosis of Rotating Machine"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (31 October 2019).

Special Issue Editors

Prof. Dr. Adam Glowacz
E-Mail Website
Guest Editor
Department of Automatic, Control and Robotics, AGH University of Science and Technology, 30-059 Kraków, Poland
Interests: machine; fault diagnosis; pattern recognition; signal processing; signal analysis; image processing; computer science; automatic
Special Issues and Collections in MDPI journals
Prof. Zhixiong Li
E-Mail Website
Guest Editor
School of Mechanical & Manufacturing Engineering The University of New South Wales, Sydney (Australia)
Interests: fault diagnosis; vibration analysis; measurement; mechanical engineering; diesel engines
Special Issues and Collections in MDPI journals
Prof. Dr. Jose Alfonso Antonino Daviu
E-Mail Website
Guest Editor
Universitat de València: VALENCIA, Spain
Interests: electric motors; fault diagnosis; transient analysis; signal processing; wavelet analysis; infrared thermography; time-frequency transforms
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue invites original research papers that report on the state-of-the-art and recent advancements in fault diagnosis of rotating machine. Rotating machines are often used in industry, for example in oil refinery, pump oil, steel mill, mine, compressor, DC motor, synchronous motor, generator, fan, motor cycle, car, vehicles, railways, steel industries, power plants, mining and fuel industries etc.. A degradation of rotating machines depends on environment and operation time. Accidents, financial loss, unscheduled downtimes can be predicted based on fault diagnosis. The scope of this Special Issue encompasses applications in Engineering, Electrical Engineering, Measurement, Signal processing and analysis, Reliability. Review articles related to fault diagnosis and prognosis are also encouraged.

Prof. Dr. Adam Glowacz
Prof. Dr. Grzegorz Krolczyk
Prof. Dr. Zhixiong Li
Prof. Dr. Jose Alfonso Antonino Daviu
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • signal processing
  • pattern recogniton
  • reliability
  • measurement
  • fault diagnosis
  • rotating machine

Published Papers (15 papers)

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Research

Open AccessArticle
Linear Method for Diagnosis of Inter-Turn Short Circuits in 3-Phase Induction Motors
Appl. Sci. 2019, 9(22), 4822; https://doi.org/10.3390/app9224822 - 11 Nov 2019
Abstract
When a turn-to-turn short fault occurs in an induction motor, it will be accompanied by vibration and heating, which will have adverse effects on the entire power system. Thus, turn-to-turn short fault diagnosis of the stator is required, and major accidents can be [...] Read more.
When a turn-to-turn short fault occurs in an induction motor, it will be accompanied by vibration and heating, which will have adverse effects on the entire power system. Thus, turn-to-turn short fault diagnosis of the stator is required, and major accidents can be prevented if an inter-turn short circuit (ITSC), which is the early stage of a turn-to-turn short, can be detected. This study reinterprets Park’s vector approach using Direct-Quadrature(D-Q) transformation for the linear separation of ITSCs and proposes an ITSC diagnosis method by defining the magnetic flux linkage pulsation and current change in the event of a turn-to-turn short. It is difficult to diagnose because the turn-to-turn short current change in an ITSC is considerably different from the induction motor loss. Hence, it was found through analysis that when the current change is considered through an analysis of the relationship between inductance and the winding number, the ITSC current becomes slightly smaller than the steady-state current. This was verified using the D-Q synchronous reference frame over time. We proposed a linear separation of the ITSC diagnosis from the steady state by considering the minimum values of the pulsating current as feature points. Full article
(This article belongs to the Special Issue Fault Diagnosis of Rotating Machine)
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Open AccessArticle
A New Concept of Instantaneous Whirling Speed for Cracked Rotor’s Axis Orbit
Appl. Sci. 2019, 9(19), 4120; https://doi.org/10.3390/app9194120 - 02 Oct 2019
Abstract
At present, the axis orbit (whirling) and the instantaneous angular speed (spinning) are important symptoms in the condition monitoring of rotor systems. However, because of the lack of research of the transient characteristics of axis orbit within a whirl cycle, the axis orbit [...] Read more.
At present, the axis orbit (whirling) and the instantaneous angular speed (spinning) are important symptoms in the condition monitoring of rotor systems. However, because of the lack of research of the transient characteristics of axis orbit within a whirl cycle, the axis orbit cannot reflect the instantaneous characteristics of the rotation during one whirling cycle like the instantaneous angular speed. Therefore, in this paper, a new concept of instantaneous whirling speed of axis orbit within a whirling cycle is proposed and defined. In addition, the transient characteristics of instantaneous whirling speed are studied. Meanwhile, the response mechanisms are qualitative analyzed through the study of the work of the additional stiffness excitation and the conversion relationship between the kinetic energy and the potential energy. Then, the minimum of the relative instantaneous whirling speed (RWS) is proposed as a potential monitoring index for crack severity. The instantaneous whirling speed is a new attribute of axis orbit and a new perspective for the vibration analysis of cracked rotors. The addition of this new attribute significantly increases the effect of axis orbit for distinguishing normal and cracked rotors. The new analysis perspective and the new diagnosis index are potential supplements for crack diagnosis. Full article
(This article belongs to the Special Issue Fault Diagnosis of Rotating Machine)
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Open AccessArticle
Time Frequency Representation Enhancement via Frequency Matching Linear Transform for Bearing Condition Monitoring under Variable Speeds
Appl. Sci. 2019, 9(18), 3828; https://doi.org/10.3390/app9183828 - 12 Sep 2019
Abstract
Instantaneous frequency (IF) of shaft rotation is pivotal for bearing fault diagnosis under variable speed operations. However, shaft IF cannot always be measured as tachometers are not allowed to be installed in every case due to design reasons and cost concerns. Extracting the [...] Read more.
Instantaneous frequency (IF) of shaft rotation is pivotal for bearing fault diagnosis under variable speed operations. However, shaft IF cannot always be measured as tachometers are not allowed to be installed in every case due to design reasons and cost concerns. Extracting the shaft IF ridge from time frequency representation (TFR) of vibration signals, therefore, becomes an alternative. Linear transform (LT), such as short time Fourier transform (STFT), has been widely adopted for such a purpose. Nevertheless, the accuracy of extracted IF ridges relies on the readability of TFR. Unfortunately, readability of TFR from STFT is often impaired by the smearing effect caused by non-synchronous frequencies between bases and signal components and limited time frequency resolution capability, which in turn adversely influences the accuracy of IF ridge extraction. To accurately extract IF ridges from vibration signals, this paper focuses on the first factor, which causes the smearing problem, and proposes a method named frequency matching linear transform (FMLT) to enhance the TFR, where transforming bases with frequencies varying with the shaft IF are constructed to alleviate the smearing effects. To construct the transforming bases with frequencies synchronous with shaft IF, a fast path optimization (FPO) algorithm, which generates all possible optimization paths among amplitude peaks and thereby ensures the continuity of extracted IF ridges, is adopted for IF pre-estimation. The TFR with improved readability can be subsequently obtained via FMLT, paving the way for accurate IF ridge extraction. Then, multiple IF ridges can be iteratively extracted using the FPO algorithm. The accuracy of extracted IF ridges before and after TFR enhancement is compared, indicating that the proposed FMLT can enhance the readability of TFR and lead to more accurate IF ridge extraction for bearing condition monitoring. Full article
(This article belongs to the Special Issue Fault Diagnosis of Rotating Machine)
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Open AccessArticle
Analysis of a Main Cabin Ventilation System in a Jack-Up Offshore Platform Part I: Numerical Modelling
Appl. Sci. 2019, 9(15), 3185; https://doi.org/10.3390/app9153185 - 05 Aug 2019
Abstract
This work aims to measure the thermodynamics of a main cabin ventilation system in a JU-2000E jack-up offshore platform. A three-dimensional (3D) physical model of the ventilation system was established, and the computational fluid dynamics (CFD) software (ANSYS FLUENT) was used to calculate [...] Read more.
This work aims to measure the thermodynamics of a main cabin ventilation system in a JU-2000E jack-up offshore platform. A three-dimensional (3D) physical model of the ventilation system was established, and the computational fluid dynamics (CFD) software (ANSYS FLUENT) was used to calculate the model thermodynamics. Numerical analysis was performed to investigate the influence mechanisms of the ventilation factors such as ventilation temperature and volume on the ventilation performance. The analysis results demonstrate that (1) top-setting of the exhaust vents is more effective than the side-setting in terms of high temperature reduction, (2) small ventilation temperature and volume can improve the ventilation efficiency, and (3) proper shutdown selection of the backup diesel engine can enhance the ventilation performance. Furthermore, the effect of humidity for the ventilation air was investigated. Lastly, an experimental platform was developed based on the simulation model. Experimental tests were carried out to evaluate the shutdown selection of the backup engine and have shown consistent results to that of the simulation model. The findings of this study provide valuable guidance in designing the ventilation system in the JU-2000E jack-up offshore platform. Full article
(This article belongs to the Special Issue Fault Diagnosis of Rotating Machine)
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Open AccessArticle
Fault Diagnosis of Rolling Bearings in Rail Train Based on Exponential Smoothing Predictive Segmentation and Improved Ensemble Learning Algorithm
Appl. Sci. 2019, 9(15), 3143; https://doi.org/10.3390/app9153143 - 02 Aug 2019
Cited by 1
Abstract
The rolling bearing is a key component of the bogie of the rail train. The working environment is complex, and it is easy to cause cracks and other faults. Effective rolling bearing fault diagnosis can provide an important guarantee for the safe operation [...] Read more.
The rolling bearing is a key component of the bogie of the rail train. The working environment is complex, and it is easy to cause cracks and other faults. Effective rolling bearing fault diagnosis can provide an important guarantee for the safe operation of the track while improving the resource utilization of the rolling bearing and greatly reducing the cost of operation. Aiming at the problem that the characteristics of the vibration data of the rolling bearing components of the railway train and the vibration mechanism model are difficult to establish, a method for long-term faults diagnosis of the rolling bearing of rail trains based on Exponential Smoothing Predictive Segmentation and Improved Ensemble Learning Algorithm is proposed. Firstly, the sliding time window segmentation algorithm of exponential smoothing is used to segment the rolling bearing vibration data, and then the segmentation points are used to construct the localized features of the data. Finally, an Improved AdaBoost Algorithm (IAA) is proposed to enhance the anti-noise ability. IAA, Back Propagation (BP) neural network, Support Vector Machine (SVM), and AdaBoost are used to classify the same dataset, and the evaluation indexes show that the IAA has the best classification effect. The article selects the raw data of the bearing experiment platform provided by the State Key Laboratory of Rail Traffic Control and Safety of Beijing Jiaotong University and the standard dataset of the American Case Western Reserve University for the experiment. Theoretical analysis and experimental results show the effectiveness and practicability of the proposed method. Full article
(This article belongs to the Special Issue Fault Diagnosis of Rotating Machine)
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Open AccessArticle
Failure Diagnosis of Demagnetization in Interior Permanent Magnet Synchronous Motors Using Vibration Characteristics
Appl. Sci. 2019, 9(15), 3111; https://doi.org/10.3390/app9153111 - 01 Aug 2019
Abstract
The detection of a precursor to the demagnetization of permanent magnets is very important because a high degree of reliability is necessary in permanent magnet synchronous motors (PMSMs). This paper investigated the diagnosis of very slight PM demagnetization. A part of the permanent [...] Read more.
The detection of a precursor to the demagnetization of permanent magnets is very important because a high degree of reliability is necessary in permanent magnet synchronous motors (PMSMs). This paper investigated the diagnosis of very slight PM demagnetization. A part of the permanent magnet was altered to non-magnetic material so as to mimic the effect of demagnetization. The vibration characteristics were clarified for low demagnetization in PMSMs driven under vector control by experiments and 3D finite element (FE) analysis. We found that the amplitude of some components of the vibration was approximately proportional to the demagnetization level of the PM and the load torque. Therefore, the measurement of vibration and torque is very useful for the estimation of the magnetization level of PMSMs under vector control except for under very light load. Full article
(This article belongs to the Special Issue Fault Diagnosis of Rotating Machine)
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Open AccessArticle
Fault Diagnosis of Induction Motor Using Convolutional Neural Network
Appl. Sci. 2019, 9(15), 2950; https://doi.org/10.3390/app9152950 - 24 Jul 2019
Cited by 1
Abstract
Induction motors are among the most important components of modern machinery and industrial equipment. Therefore, it is necessary to develop a fault diagnosis system that detects the operating conditions of and faults in induction motors early. This paper presents an induction motor fault [...] Read more.
Induction motors are among the most important components of modern machinery and industrial equipment. Therefore, it is necessary to develop a fault diagnosis system that detects the operating conditions of and faults in induction motors early. This paper presents an induction motor fault diagnosis system based on a CNN (convolutional neural network) model. In the proposed method, vibration signal data are obtained from the induction motor experimental environment, and these values are input into the CNN. Then, the CNN performs fault diagnosis. In this study, fault diagnosis of an induction motor is performed in three states, namely, normal, rotor fault, and bearing fault. In addition, a GUI (graphical user interface) for the proposed fault diagnosis system is presented. The experimental results confirm that the proposed method is suitable for diagnosing rotor and bearing faults of induction motors. Full article
(This article belongs to the Special Issue Fault Diagnosis of Rotating Machine)
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Open AccessArticle
Fault Diagnosis of Rolling Bearing Based on Multiscale Intrinsic Mode Function Permutation Entropy and a Stacked Sparse Denoising Autoencoder
Appl. Sci. 2019, 9(13), 2743; https://doi.org/10.3390/app9132743 - 06 Jul 2019
Cited by 1
Abstract
Effective intelligent fault diagnosis of bearings is important for improving safety and reliability of machine. Benefiting from the training advantages, deep learning method can automatically and adaptively learn more abstract and high-level features without much priori knowledge. To realize representative features mining and [...] Read more.
Effective intelligent fault diagnosis of bearings is important for improving safety and reliability of machine. Benefiting from the training advantages, deep learning method can automatically and adaptively learn more abstract and high-level features without much priori knowledge. To realize representative features mining and automatic recognition of bearing health condition, a diagnostic model of stacked sparse denoising autoencoder (SSDAE) which combines sparse autoencoder (SAE) and denoising autoencoder (DAE) is proposed in this paper. The sparse criterion in SAE, corrupting operation in DAE and reasonable designing of the stack order of autoencoders help to mine essential information of the input and improve fault pattern classification robustness. In order to provide better input features for the constructed network, the raw non-stationary and nonlinear vibration signals are processed with ensemble empirical mode decomposition (EEMD) and multiscale permutation entropy (MPE). MPE features which are extracted based on both the selected characteristic frequency-related intrinsic mode function components (IMFs) and the raw signal, are used as low-level feature for the input of the proposed diagnostic model for health condition recognition and classification. Two experiments based on the Case Western Reserve University (CWRU) dataset and the measurement dataset from laboratory were conducted, and results demonstrate the effectiveness of the proposed method and highlight its excellent performance relative to existing methods. Full article
(This article belongs to the Special Issue Fault Diagnosis of Rotating Machine)
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Open AccessArticle
Application of Multi-Dimension Input Convolutional Neural Network in Fault Diagnosis of Rolling Bearings
Appl. Sci. 2019, 9(13), 2690; https://doi.org/10.3390/app9132690 - 01 Jul 2019
Cited by 1
Abstract
Aiming at the problem of poor robustness of the intelligent diagnostic model, a fault diagnosis model of rolling bearing based on a multi-dimension input convolutional neural network (MDI-CNN) is proposed. Compared with the traditional convolution neural network, the proposed model has multiple input [...] Read more.
Aiming at the problem of poor robustness of the intelligent diagnostic model, a fault diagnosis model of rolling bearing based on a multi-dimension input convolutional neural network (MDI-CNN) is proposed. Compared with the traditional convolution neural network, the proposed model has multiple input layers. Therefore, it can fuse the original signal and processed signal—making full use of advantages of the convolutional neural networks to learn the original signal characteristics automatically, and also improving recognition accuracy and anti-jamming ability. The feasibility and validity of the proposed MDI-CNN are verified, and its advantages are proved by comparison with the other related models. Moreover, the robustness of the model is tested by adding the noise to the test set. Finally, the stability of the model is verified by two experiments. The experimental results show that the proposed model improves the recognition rate, robustness and convergence performance of the traditional convolution model and has good generalization ability. Full article
(This article belongs to the Special Issue Fault Diagnosis of Rotating Machine)
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Open AccessArticle
Detecting and Learning Unknown Fault States by Automatically Finding the Optimal Number of Clusters for Online Bearing Fault Diagnosis
Appl. Sci. 2019, 9(11), 2326; https://doi.org/10.3390/app9112326 - 06 Jun 2019
Cited by 2
Abstract
This paper proposes an online fault diagnosis system for bearings that detect emerging fault modes and then updates the diagnostic system knowledge (DSK) to incorporate information about the newly detected fault modes. New fault modes are detected using k-means clustering along with a [...] Read more.
This paper proposes an online fault diagnosis system for bearings that detect emerging fault modes and then updates the diagnostic system knowledge (DSK) to incorporate information about the newly detected fault modes. New fault modes are detected using k-means clustering along with a new cluster evaluation method, i.e., multivariate probability density function’s cluster distribution factor (MPDFCDF). In this proposed model, a heterogeneous pool of features is constructed from the signal. A hybrid feature selection model is adopted for selecting optimal feature for learning the model with existing fault mode. The proposed online fault diagnosis system detects new fault modes from unknown signals using k-means clustering with the help of proposed MPDFCDF cluster evaluation method. The DSK is updated whenever new fault modes are detected and updated DSK is used to classify faults using the k-nearest neighbor (k-NN) classifier. The proposed model is evaluated using acoustic emission signals acquired from low-speed rolling element bearings with different fault modes and severities under different rotational speeds. Experimental results present that the MPDFCDF cluster evaluation method can detect the optimal number of fault clusters, and the proposed online diagnosis model can detect newly emerged faults and update the DSK effectively, which improves the diagnosis performance in terms of the average classification performance. Full article
(This article belongs to the Special Issue Fault Diagnosis of Rotating Machine)
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Open AccessArticle
Improving the Accuracy of Fault Frequency by Means of Local Mean Decomposition and Ratio Correction Method for Rolling Bearing Failure
Appl. Sci. 2019, 9(9), 1888; https://doi.org/10.3390/app9091888 - 08 May 2019
Cited by 1
Abstract
The fault frequencies are as they are and cannot be improved. One can only improve its estimation quality. This paper proposes a fault diagnosis method by combining local mean decomposition (LMD) and the ratio correction method to process the short-time signals. Firstly, the [...] Read more.
The fault frequencies are as they are and cannot be improved. One can only improve its estimation quality. This paper proposes a fault diagnosis method by combining local mean decomposition (LMD) and the ratio correction method to process the short-time signals. Firstly, the vibration signal of rolling bearing is decomposed into a series of product functions (PFs) by LMD. The PF, which contains the richest fault information, is selected to perform envelope spectrum analysis by the Hilbert transform (HT). Secondly, the Hilbert envelope spectrum of the selected PF is corrected with the ratio correction method. Finally, higher precision fault frequencies are extracted from the corrected Hilbert envelope spectrum, and then the fault location is accurately determined. The proposed method of this paper can be used in online real-time monitoring technology of rolling bearing failure. Full article
(This article belongs to the Special Issue Fault Diagnosis of Rotating Machine)
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Open AccessArticle
Nonlinear Blind Source Separation and Fault Feature Extraction Method for Mining Machine Diagnosis
Appl. Sci. 2019, 9(9), 1852; https://doi.org/10.3390/app9091852 - 06 May 2019
Cited by 1
Abstract
Mining machines are strongly nonlinear systems, and their transmission vibration signals are nonlinear mixtures of different kinds of vibration sources. In addition, vibration signals measured by the accelerometer are contaminated by noise. As a result, it is inefficient and ineffective for the blind [...] Read more.
Mining machines are strongly nonlinear systems, and their transmission vibration signals are nonlinear mixtures of different kinds of vibration sources. In addition, vibration signals measured by the accelerometer are contaminated by noise. As a result, it is inefficient and ineffective for the blind source separation (BSS) algorithm to separate the critical independent sources associated with the transmission fault vibrations. For this reason, a new method based on wavelet de-noising and nonlinear independent component analysis (ICA) is presented in this paper to tackle the nonlinear BSS problem with additive noise. The wavelet de-noising approach was first employed to eliminate the influence of the additive noise in the BSS procedure. Then, the radial basis function (RBF) neural network combined with the linear ICA was applied to the de-noised vibration signals. Vibration sources involved with the machine faults were separated. Subsequently, wavelet package decomposition (WPD) was used to extract distinct fault features from the source signals. Lastly, an RBF classifier was used to recognize the fault patterns. Field data acquired from a mining machine was used to evaluate and validate the proposed diagnostic method. The experimental analysis results show that critical fault vibration source component can be separated by the proposed method, and the fault detection rate is superior to the linear ICA based approaches. Full article
(This article belongs to the Special Issue Fault Diagnosis of Rotating Machine)
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Open AccessArticle
Compound Faults Feature Extraction for Rolling Bearings Based on Parallel Dual-Q-Factors and the Improved Maximum Correlated Kurtosis Deconvolution
Appl. Sci. 2019, 9(8), 1681; https://doi.org/10.3390/app9081681 - 23 Apr 2019
Cited by 1
Abstract
Vibration analysis is one of the main effective ways for rolling bearing fault diagnosis, and a challenge is how to accurately separate the inner and outer race fault features from noisy compound faults signals. Therefore, a novel compound fault separation algorithm based on [...] Read more.
Vibration analysis is one of the main effective ways for rolling bearing fault diagnosis, and a challenge is how to accurately separate the inner and outer race fault features from noisy compound faults signals. Therefore, a novel compound fault separation algorithm based on parallel dual-Q-factors and improved maximum correlation kurtosis deconvolution (IMCKD) is proposed. First, the compound fault signal is sparse-decomposed by the parallel dual-Q-factor, and the low-resonance components of the signal (compound fault impact component and small amount of noise) are obtained, but it can only highlight the impact of compound faults, and failed to separate the inner and outer race compound fault signal. Then, the MCKD is improved (IMCKD) by optimizing the selection of parameters (the shift order M and the filter length L) based on the iterative calculation method with the Teager envelope spectral kurtosis (TEK) index. Finally, after the composite fault signal is filtered and de-noised by the proposed method, the inner and outer race fault signals are obtained respectively. The fault characteristic frequency is consistent with the theoretical calculation value. The results show that the proposed method can efficiently separate the mixed fault information and avoid the mutual interference between the components of the compound fault. Full article
(This article belongs to the Special Issue Fault Diagnosis of Rotating Machine)
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Open AccessArticle
Dependency Model-Based Multiple Fault Diagnosis Using Knowledge of Test Result and Fault Prior Probability
Appl. Sci. 2019, 9(2), 311; https://doi.org/10.3390/app9020311 - 16 Jan 2019
Abstract
Aiming at solving the multiple fault diagnosis problem as well as the sequence of all the potential multiple faults simultaneously, a new multiple fault diagnosis method based on the dependency model method as well as the knowledge in test results and the prior [...] Read more.
Aiming at solving the multiple fault diagnosis problem as well as the sequence of all the potential multiple faults simultaneously, a new multiple fault diagnosis method based on the dependency model method as well as the knowledge in test results and the prior probability of each fault type is proposed. Firstly, the dependency model of the system can be built and used to formulate the fault-test dependency matrix. Then, the dependency matrix is simplified according to the knowledge in the test results of the system. After that, the logic ‘OR’ operation is performed on the feature vectors of the fault status in the simplified dependency matrix to formulate the multiple fault dependency matrix. Finally, fault diagnosis is based on the multiple fault dependency matrix and the ranking of each fault type calculated basing on the prior probability of each fault status. An illustrative numerical example and a case study are presented to verify the effectiveness and superiority of the proposed method. Full article
(This article belongs to the Special Issue Fault Diagnosis of Rotating Machine)
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Open AccessFeature PaperArticle
Recognition of Acoustic Signals of Commutator Motors
Appl. Sci. 2018, 8(12), 2630; https://doi.org/10.3390/app8122630 - 15 Dec 2018
Cited by 12
Abstract
Most faults can stop a motor, and time is lost in fixing the damaged motor. This is a reason why it is essential to develop fault-detection methods. This paper describes the acoustic-based fault detection of two commutator motors: the commutator motor of an [...] Read more.
Most faults can stop a motor, and time is lost in fixing the damaged motor. This is a reason why it is essential to develop fault-detection methods. This paper describes the acoustic-based fault detection of two commutator motors: the commutator motor of an electric impact drill and the commutator motor of a blender. Acoustic signals were recorded by a smartphone. Five states of the electric impact drill and three states of the blender were analysed: for the electric impact drill, these states were healthy, damaged gear train, faulty fan with five broken rotor blades, faulty fan with 10 broken rotor blades, and shifted brush (motor off); for the blender, these states were healthy, faulty fan with two broken rotor blades, and faulty fan with five broken rotor blades. A feature extraction method, MSAF-RATIO-27-MULTIEXPANDED-4-GROUPS (Method of Selection of Amplitudes of Frequency Ratio of 27% Multiexpanded 4 Groups), was developed and used for the computation of feature vectors. The nearest mean (NM) and support vector machine (SVM) classifiers were used for data classification. Analysis of the recognition of acoustic signals was carried out. The analysed value of TEEID (the total efficiency of recognition of the electric impact drill) was equal to 96% for the NM classifier and 88.8% for SVM. The analysed value of TEB (the total efficiency of recognition of the blender) was equal to 100% for the NM classifier and 94.11% for SVM. Full article
(This article belongs to the Special Issue Fault Diagnosis of Rotating Machine)
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