Fault Diagnosis of Induction Machines in Transient Regime Using Current Sensors with an Optimized Slepian Window

The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite duration the Slepian window has the maximum concentration of energy, greater than can be reached with a gated Gaussian window, which is usually used as analysis window. In this paper the use and optimization of the Slepian window for fault diagnosis of induction machines is theoretically introduced and experimentally validated through the test of a 3.15 MW induction motor with broken bars during the start-up transient. The theoretical analysis and the experimental results show that the use of the Slepian window can highlight the fault components in the current's spectrogram with a significant reduction of the required computational resources.

Download Full-text

Fault Diagnosis of Induction Machines in Transient Regime Using Current Sensors with an Optimized Slepian Window

10.20944/preprints201712.0026.v1 ◽

2017 ◽

Author(s):

Jordi Burriel-Valencia ◽

Ruben Puche-Panadero ◽

Javier Martinez-Roman ◽

Angel Sapena-Baño ◽

Manuel Pineda-Sanchez

Keyword(s):

Fault Diagnosis ◽

Induction Machines ◽

Transient Regime ◽

Sequence Length ◽

Time Frequency ◽

Stator Current ◽

Start Up ◽

Analysis Window ◽

Computational Resources ◽

Short Time

Download Full-text

Fault Diagnosis in the Slip–Frequency Plane of Induction Machines Working in Time-Varying Conditions

Sensors ◽

10.3390/s20123398 ◽

2020 ◽

Vol 20 (12) ◽

pp. 3398 ◽

Cited By ~ 1

Author(s):

Ruben Puche-Panadero ◽

Javier Martinez-Roman ◽

Angel Sapena-Bano ◽

Jordi Burriel-Valencia ◽

Martin Riera-Guasp

Keyword(s):

Fault Diagnosis ◽

Single Point ◽

General Scheme ◽

Induction Machine ◽

Induction Machines ◽

Transient Conditions ◽

Time Frequency ◽

Bearing Defects ◽

Motor Current Signature Analysis ◽

The Hilbert Transform

Motor current signature analysis (MCSA) is a fault diagnosis method for induction machines (IMs) that has attracted wide industrial interest in recent years. It is based on the detection of the characteristic fault signatures that arise in the current spectrum of a faulty induction machine. Unfortunately, the MCSA method in its basic formulation can only be applied in steady state functioning. Nevertheless, every day increases the importance of inductions machines in applications such as wind generation, electric vehicles, or automated processes in which the machine works most of time under transient conditions. For these cases, new diagnostic methodologies have been proposed, based on the use of advanced time-frequency transforms—as, for example, the continuous wavelet transform, the Wigner Ville distribution, or the analytic function based on the Hilbert transform—which enables to track the fault components evolution along time. All these transforms have high computational costs and, furthermore, generate as results complex spectrograms, which require to be interpreted for qualified technical staff. This paper introduces a new methodology for the diagnosis of faults of IM working in transient conditions, which, unlike the methods developed up to today, analyzes the current signal in the slip-instantaneous frequency plane (s-IF), instead of the time-frequency (t-f) plane. It is shown that, in the s-IF plane, the fault components follow patterns that that are simple and unique for each type of fault, and thus does not depend on the way in which load and speed vary during the transient functioning; this characteristic makes the diagnostic task easier and more reliable. This work introduces a general scheme for the IMs diagnostic under transient conditions, through the analysis of the stator current in the s-IF plane. Another contribution of this paper is the introduction of the specific s-IF patterns associated with three different types of faults (rotor asymmetry fault, mixed eccentricity fault, and single-point bearing defects) that are theoretically justified and experimentally tested. As the calculation of the IF of the fault component is a key issue of the proposed diagnostic method, this paper also includes a comparative analysis of three different mathematical tools for calculating the IF, which are compared not only theoretically but also experimentally, comparing their performance when are applied to the tested diagnostic signals.

Download Full-text

Fault Diagnosis of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window

Sensors ◽

10.3390/s18010146 ◽

2018 ◽

Vol 18 (2) ◽

pp. 146 ◽

Cited By ~ 12

Author(s):

◽

Keyword(s):

Fault Diagnosis ◽

Induction Machines ◽

Transient Regime ◽

Current Sensors

Download Full-text

Remaining Useful Life Prediction and Fault Diagnosis of Rolling Bearings Based on Short-Time Fourier Transform and Convolutional Neural Network

Shock and Vibration ◽

10.1155/2020/8857307 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Shuang Zhou ◽

Maohua Xiao ◽

Petr Bartos ◽

Martin Filip ◽

Guosheng Geng

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Life Prediction ◽

Remaining Useful Life ◽

Fault Identification ◽

Rolling Bearings ◽

Time Frequency ◽

Diagnosis Method ◽

Useful Life ◽

Short Time

Rolling bearings play a pivotal role in rotating machinery. The remaining useful life prediction and fault diagnosis of bearings are crucial to condition-based maintenance. However, traditional data-driven methods usually require manual extraction of features, which needs rich signal processing theory as support and is difficult to control the efficiency. In this study, a bearing remaining life prediction and fault diagnosis method based on short-time Fourier transform (STFT) and convolutional neural network (CNN) has been proposed. First, the STFT was adopted to construct time-frequency maps of the unprocessed original vibration signals that can ensure the true and effective recovery of the fault characteristics in vibration signals. Then, the training time-frequency maps were used as an input of the CNN to train the network model. Finally, the time-frequency maps of testing signals were inputted into the network model to complete the life prediction or fault identification of rolling bearings. The rolling bearing life-cycle datasets from the Intelligent Management System were applied to verify the proposed life prediction method, showing that its accuracy reaches 99.45%, and the prediction effect is good. Multiple sets of validation experiments were conducted to verify the proposed fault diagnosis method with the open datasets from Case Western Reserve University. Results show that the proposed method can effectively identify the fault classification and the accuracy can reach 95.83%. The comparison with the fault diagnosis classification effects of backpropagation (BP) neural network, particle swarm optimization-BP, and genetic algorithm-BP further proves its superiority. The proposed method in this paper is proved to have strong ability of adaptive feature extraction, life prediction, and fault identification.

Download Full-text

Using ASTFT Spectrum to Suppress Cross Terms in WVD and its Application in Fault Diagnosis

Materials Science Forum ◽

10.4028/www.scientific.net/msf.626-627.535 ◽

2009 ◽

Vol 626-627 ◽

pp. 535-540

Author(s):

B.P. Tang ◽

F. Li ◽

W.Y. Liu

Keyword(s):

Feature Extraction ◽

Fourier Transform ◽

Fault Diagnosis ◽

Short Time Fourier Transform ◽

Time Frequency ◽

Effective Suppression ◽

Cross Terms ◽

Diagnosis Method ◽

Simulation Results ◽

Short Time

A new fault diagnosis method to suppress cross terms of Wigner-Ville distribution (WVD) using Adaptive Short-time Fourier Transform (ASTFT) spectrum is put forward. The relationships of correlation between auto terms and cross terms of WVD are obtained theoretically by analyzing the WVD. Firstly, the signal ASTFT spectrum which can determine the signal component positions in the time-frequency plane is obtained. Then, the ASTFT spectrum as a window function is selected to process the signal WVD. Thus the cross terms can be effectively restrained. The simulation results show that a better resolution and more effective suppression of cross terms can be obtained. At last, the proposed method is applied to the fault diagnosis of bearing. The simulation and the experiment results indicate that the proposed method is effective in feature extraction.

Download Full-text

Lightweight Convolutional Neural Network and Its Application in Rolling Bearing Fault Diagnosis under Variable Working Conditions

Sensors ◽

10.3390/s19224827 ◽

2019 ◽

Vol 19 (22) ◽

pp. 4827 ◽

Cited By ~ 8

Author(s):

Hengchang Liu ◽

Dechen Yao ◽

Jianwei Yang ◽

Xi Li

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Rolling Bearing ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Time Domain Signal ◽

The One ◽

Short Time

The rolling bearing is an important part of the train’s running gear, and its operating state determines the safety during the running of the train. Therefore, it is important to monitor and diagnose the health status of rolling bearings. A convolutional neural network is widely used in the field of fault diagnosis because it does not require feature extraction. Considering that the size of the network model is large and the requirements for monitoring equipment are high. This study proposes a novel bearing fault diagnosis method based on lightweight network ShuffleNet V2 with batch normalization and L2 regularization. In the experiment, the one-dimensional time-domain signal is converted into a two-dimensional Time-Frequency Graph (TFG) using a short-time Fourier transform, though the principle of graphics to enhance the TFG dataset. The model mainly consists of two units, one for extracting features and one for spatial down-sampling. The building units are repeatedly stacked to construct the whole model. By comparing the proposed method with the origin ShuffleNet V2, machine learning model and state-of-the-art fault diagnosis model, the generalization of the proposed method for bearing fault diagnosis is verified.

Download Full-text

Short-Frequency Fourier Transform for Fault Diagnosis of Induction Machines Working in Transient Regime

IEEE Transactions on Instrumentation and Measurement ◽

10.1109/tim.2016.2647458 ◽

2017 ◽

Vol 66 (3) ◽

pp. 432-440 ◽

Cited By ~ 36

Author(s):

Jordi Burriel-Valencia ◽

Ruben Puche-Panadero ◽

Javier Martinez-Roman ◽

Angel Sapena-Bano ◽

Manuel Pineda-Sanchez

Keyword(s):

Fourier Transform ◽

Fault Diagnosis ◽

Induction Machines ◽

Transient Regime

Download Full-text

An Intelligent Bearing Fault Diagnosis Method Based on SF-SVM

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1210/1/012004 ◽

2021 ◽

Vol 1210 (1) ◽

pp. 012004

Author(s):

Bao’an Qiu ◽

Pan Sun ◽

Lili Li

Keyword(s):

Fault Diagnosis ◽

Rolling Bearing ◽

Support Vector ◽

Time Frequency ◽

Diagnosis Method ◽

Bearing Vibration ◽

Frequency Map ◽

The Stability ◽

Vibration Time ◽

Short Time

Abstract Rolling bearing, as a key component of rotating machinery, its health status directly determines the stability and reliability of the whole machine. The research on its intelligent diagnosis method has important engineering value and academic significance. However, due to actual engineering conditions, the types of bearing failures and the amount of data are limited. Aiming at the difficulty of extracting and selecting bearing vibration features under limited sample constraints, this pa-per proposes an intelligent fault diagnosis method of SF-SVM. On the basis of the short-time Fourier change, the L2 regularized sparse filter is used to extract the unsupervised feature of the bearing vibration time-frequency map. After obtaining the typical features of the bearing, the support vector machine is used for diagnosis.

Download Full-text

Cost-Effective Reduced Envelope of the Stator Current via Synchronous Sampling for the Diagnosis of Rotor Asymmetries in Induction Machines Working at Very Low Slip

Sensors ◽

10.3390/s19163471 ◽

2019 ◽

Vol 19 (16) ◽

pp. 3471 ◽

Cited By ~ 4

Author(s):

Jordi Burriel-Valencia ◽

Ruben Puche-Panadero ◽

Javier Martinez-Roman ◽

Angel Sapena-Bano ◽

Manuel Pineda-Sanchez

Keyword(s):

Fault Diagnosis ◽

Low Cost ◽

Computing Time ◽

Low Frequency ◽

Cost Effective ◽

Induction Machines ◽

Minimal Amount ◽

Stator Current ◽

Current Spectrum ◽

On Line

Fault diagnosis of rotor asymmetries of IM using the stator current relies on the detection of the characteristic signatures of the fault harmonics in the current spectrum. In some scenarios, such as large induction machines running at a very low slip, or unloaded machines tested offline, this technique may fail. In these scenarios, the fault harmonics are very close to the frequency of the fundamental component, and have a low amplitude, so that they may remain undetected, buried under the fundamental’s leakage, until the damage is severe. To avoid false positives, a proven approach is to search for the fault harmonics in the current envelope, instead of the current itself, because in this case the spectrum is free from the leakage of the fundamental. Besides, the fault harmonics appear at a very low frequency. Nevertheless, building the current spectrum is costly in terms of computing complexity, as in the case of the Hilbert transform, or hardware resources, as in the need for simultaneously sampling three stator currents in the case of the EPVA. In this work, a novel method is proposed to avoid this problem. It is based on sampling a phase current just twice per current cycle, with a fixed delay with respect to its zero crossings. It is shown that the spectrum of this reduced set of current samples contains the same fault harmonics as the spectrum of the full-length current envelope, despite using a minimal amount of computing resources. The proposed approach is cost-effective, because the computational requirements for building the current envelope are reduced to less than 1 % of those required by other conventional methods, in terms of storage and computing time. In this way, it can be implemented with low-cost embedded devices for on-line fault diagnosis. The proposed approach is introduced theoretically and validated experimentally, using a commercial induction motor with a broken bar under different load and supply conditions. Besides, the proposed approach has been implemented on a low-cost embedded device, which can be accessed on-line for remote fault diagnosis.

Download Full-text

Automatic Fault Diagnostic System for Induction Motors under Transient Regime Optimized with Expert Systems

Electronics ◽

10.3390/electronics8010006 ◽

2018 ◽

Vol 8 (1) ◽

pp. 6 ◽

Cited By ~ 9

Author(s):

Jordi Burriel-Valencia ◽

Ruben Puche-Panadero ◽

Javier Martinez-Roman ◽

Angel Sapena-Bano ◽

Manuel Pineda-Sanchez ◽

...

Keyword(s):

Fault Diagnosis ◽

Expert Systems ◽

Induction Motors ◽

Early Stage ◽

Diagnostic System ◽

Transient Regime ◽

Economic Losses ◽

Diagnostic Systems ◽

Time Frequency ◽

Cage Induction Motor

Induction machines (IMs) power most modern industrial processes (induction motors) and generate an increasing portion of our electricity (doubly fed induction generators). A continuous monitoring of the machine’s condition can identify faults at an early stage, and it can avoid costly, unexpected shutdowns of production processes, with economic losses well beyond the cost of the machine itself. Machine current signature analysis (MCSA), has become a prominent technique for condition-based maintenance, because, in its basic approach, it is non-invasive, requires just a current sensor, and can process the current signal using a standard fast Fourier transform (FFT). Nevertheless, the industrial application of MCSA requires well-trained maintenance personnel, able to interpret the current spectra and to avoid false diagnostics that can appear due to electrical noise in harsh industrial environments. This task faces increasing difficulties, especially when dealing with machines that work under non-stationary conditions, such as wind generators under variable wind regime, or motors fed from variable speed drives. In these cases, the resulting spectra are no longer simple one-dimensional plots in the time domain; instead, they become two-dimensional images in the joint time-frequency domain, requiring highly specialized personnel to evaluate the machine condition. To alleviate these problems, supporting the maintenance staff in their decision process, and simplifying the correct use of fault diagnosis systems, expert systems based on neural networks have been proposed for automatic fault diagnosis. However, all these systems, up to the best knowledge of the authors, operate under steady-state conditions, and are not applicable in a transient regime. To solve this problem, this paper presents an automatic system for generating optimized expert diagnostic systems for fault detection when the machine works under transient conditions. The proposed method is first theoretically introduced, and then it is applied to the experimental diagnosis of broken bars in a commercial cage induction motor.

Download Full-text