Motor current signal analysis using a modified bispectrum for machine fault diagnosis

This paper presents the use of the induction motor current to identify and quantify common faults within a two-stage reciprocating compressor. The theoretical basis is studied to understand current signal characteristics when the motor undertakes a varying load under faulty conditions. Although conventional bispectrum representation of current signal allows the inclusion of phase information and the elimination of Gaussian noise, it produces unstable results due to random phase variation of the sideband components in the current signal. A modified bispectrum based on the amplitude modulation feature of the current signal is thus proposed to combine both lower sidebands and higher sidebands simultaneously and hence describe the current signal more accurately. Based on this new bispectrum a more effective diagnostic feature namely normalised bispectral peak is developed for fault classification. In association with the kurtosis of the raw current signal, the bispectrum feature gives rise to reliable fault classification results. In particular, the low feature values can differentiate the belt looseness from other fault cases and discharge valve leakage and intercooler leakage can be separated easily using two linear classifiers. This work provides a novel approach to the analysis of stator current for the diagnosis of motor drive faults from downstream driving equipment

Vibration analysis of bearing for fault detection using time domain features and neural network

Ball bearings are among the most important and frequently encountered components in the vast majority of rotating machines, their carrying capacity and reliability being prominent for the overall machine performance. Fault detection and diagnosis in the early stages of damage is necessary to prevent their malfunctioning and failure during operation. This paper presents fault detection of ball bearing using time domain features of vibration signals. The vibration signals are recorded at bearing housing of 5hp squirrel cage induction motor. These extracted features are used to train and test the neural network for four bearing conditions namely: Healthy, defective Outer race, defective Inner race and defective ball fault condition. The experimental observation shows that the proposed method is able to detect the faulty condition with high accuracy

Bibliography on Induction Motors Faults Detection and Diagnosis

International audienceThis paper provides a comprehensive list of books, workshops, conferences, and journal papers related to induction motors faults detection and diagnosis

Bearing Fault Detection by One-Dimensional Convolutional Neural Networks

Bearing faults are the biggest single source of motor failures. Artificial Neural Networks (ANNs) and other decision support systems are widely used for early detection of bearing faults. The typical decision support systems require feature extraction and classification as two distinct phases. Extracting fixed features each time may require a significant computational cost preventing their use in real-time applications. Furthermore, the selected features for the classification phase may not represent the most optimal choice. In this paper, the use of 1D Convolutional Neural Networks (CNNs) is proposed for a fast and accurate bearing fault detection system. The feature extraction and classification phases of the bearing fault detection are combined into a single learning body with the implementation of 1D CNN. The raw vibration data (signal) is fed into the proposed system as input eliminating the need for running a separate feature extraction algorithm each time vibration data is analyzed for classification. Implementation of 1D CNNs results in more efficient systems in terms of computational complexity. The classification performance of the proposed system with real bearing data demonstrates that the reduced computational complexity is achieved without a compromise in fault detection accuracy

Higher-order spectral analysis of stray flux signals for faults detection in induction motors

[EN] This work is a review of current trends in the stray flux signal processing techniques applied to the diagnosis of electrical machines. Initially, a review of the most commonly used standard methods is performed in the diagnosis of failures in induction machines and using stray flux; and then specifically it is treated and performed the algorithms based on statistical analysis using cumulants and polyspectra. In addition, the theoretical foundations of the analyzed algorithms and examples applications are shown from the practical point of view where the benefits that processing can have using HOSA and its relationship with stray flux signal analysis, are illustrated.This work has been supported by Generalitat Valenciana, Conselleria d'Educació, Cultura i Esport in the framework of the "Programa para la promoción de la investigación científica, el desarrollo tecnológico y la innovación en la Comunitat Valenciana", Subvenciones para grupos de investigación consolidables (ref: AICO/2019/224). J. Alberto Conejero is also partially supported by MEC Project MTM2016-75963-P.Iglesias Martínez, ME.; Antonino Daviu, JA.; Fernández De Córdoba, P.; Conejero, JA. (2020). Higher-order spectral analysis of stray flux signals for faults detection in induction motors. Applied Mathematics and Nonlinear Sciences. 5(2):1-14. https://doi.org/10.2478/amns.2020.1.00032S11452H. Akçay and E. Germen. 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Gear wear monitoring by modulation signal bispectrum based on motor current signal analysis

Gears are important mechanical components for power transmissions. Tooth wear is one of the most common failure modes, which can present throughout a gear’s lifetime. It is significant to accurately monitor gear wear progression in order to take timely predictive maintenances. Motor current signature analysis (MCSA) is an effective and non-intrusive approach which is able to monitor faults from both electrical and mechanical systems. However, little research has been reported in monitoring the gear wear and estimating its severity based on MCSA. This paper presents a novel gear wear monitoring method through a modulation signal bispectrum based motor current signal analysis (MSB-MCSA). For a steady gear transmission, it is inevitable to exist load and speed oscillations due to various errors including wears. These oscillations can induce small modulations in the current signals of the driving motor. MSB is particularly effective in characterising such small modulation signals. Based on these understandings, the monitoring process was implemented based on the current signals from a run-to-failure test of an industrial two stages helical gearbox under a moderate accelerated fatigue process. At the initial operation of the test, MSB analysis results showed that the peak values at the bifrequencies of gear rotations and the power supply can be effective monitoring features for identifying faulty gears and wear severity as they exhibit agreeable changes with gear loads. A monotonically increasing trend established by these features allows a clear indication of the gear wear progression. The dismantle inspection at 477 hours of operation, made when one of the monitored features is about 123% higher than its baseline, has found that there are severe scuffing wear marks on a number of tooth surfaces on the driving gear, showing that the gear endures a gradual wear process during its long test operation. Therefore, it is affirmed that the MSB-MSCA approach proposed is reliable and accurate for monitoring gear wear deterioration

State of the art and trends in the monitoring, detection and diagnosis of failures in electric induction motors

Producción CientíficaDespite the complex mathematical models and physical phenomena on which it is based, the simplicity of its construction, its affordability, the versatility of its applications and the relative ease of its control have made the electric induction motor an essential element in a considerable number of processes at the industrial and domestic levels, in which it converts electrical energy into mechanical energy. The importance of this type of machine for the continuity of operation, mainly in industry, is such that, in addition to being an important part of the study programs of careers related to this branch of electrical engineering, a large number of investigations into monitoring, detecting and quickly diagnosing its incipient faults due to a variety of factors have been conducted. This bibliographic research aims to analyze the conceptual aspects of the first discoveries that served as the basis for the invention of the induction motor, ranging from the development of the Fourier series, the Fourier transform mathematical formula in its different forms and the measurement, treatment and analysis of signals to techniques based on artificial intelligence and soft computing. This research also includes topics of interest such as fault types and their classification according to the engine, software and hardware parts used and modern approaches or maintenance strategies

Fault detection and diagnosis method for three-phase induction motor

Induction motors (IM) are critical components in many industrial processes. There is a continually increasing interest in the IMs’ fault diagnosis. The scope of this thesis involves condition monitoring and fault detection of three phase IMs. Different monitoring techniques have been used for fault detection on IMs. Vibration and stator current monitoring have gained privilege in literature and in the industry for fault diagnosis. The performance of the vibration and stator current setups was compared and evaluated. In that perspective, a number of data were captured from different faulty and healthy IMs by vibration and current sensors. The Principal Component Analysis (PCA) was utilized for feature extraction to monitor and classify collected data for finding the faults in IMs. A new method was proposed with the combined use of vibration and current setups for fault detection. It consists of two steps: firstly, the training part with the aim of giving acceleration property (nature of vibration data) to the current features, and secondly the testing part with the aim of excluding the vibration setup from the fault detection algorithm, while the output data have the property of vibration features. The 0-1 loss function was applied to show the accuracy of vibration, current and proposed fault detection method. The PCA classified results showed mixed and unseparated features for the current setup. The vibration setup and the proposed method resulted in substantial classified features. The 0-1 loss function results showed that the vibration setup and the developed method can provide a good level of accuracy. The vibration setup attained the highest accuracy of 98.2% in training and 92% in testing. The proposed method performed well with accuracies of 96.5% in training and 84% in testing. The current setup, however, attained the lowest level of accuracy (66.7% in training and 52% in testing). To assess the performance of the proposed method, the Confusion matrix of classification in NN was utilized. The Confusion matrix showed an accuracy of 95.1% of accuracy and negligible incorrect responses (4.9%), meaning that the proposed fault detection method is reliable with minimum possible errors. These vibration, current and proposed fault detection methods were also evaluated in terms of cost. The proposed method provided an affordable fault detection technique with a high accuracy applicable in various industrial fields

Deep Learning Model for Industrial Leakage Detection Using Acoustic Emission Signal

Intelligent fault diagnosis methods have replaced time consuming and unreliable human analysis, increasing anomaly detection efficiency. Deep learning models are clear cut techniques for this purpose. This paper’s fundamental purpose is to automatically detect leakage in tanks during production with more reliability than a manual inspection, a common practice in industries. This research proposes an inspection system to predict tank leakage using hydrophone sensor data and deep learning algorithms after production. In this paper, leak detection was investigated using an experimental setup consisting of a plastic tank immersed underwater. Three different techniques for this purpose were implemented and compared with each other, including fast Fourier transform (FFT), wavelet transforms, and time-domain features, all of which are followed with 1D convolution neural network (1D-CNN). Applying FFT and converting the signal to a 1D image followed by 1D-CNN showed better results than other methods. Experimental results demonstrate the effectiveness and the superiority of the proposed methodology for detecting real-time leakage inaccuracy