Multiple Combined Faults Research Articles

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM.

Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis.

The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN) architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC) method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs). The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance.

Reliable multiple combined fault diagnosis of bearings using heterogeneous feature models and multiclass support vector Machines

This paper proposes a reliable multiple combined fault diagnosis scheme for bearings using heterogeneous feature models and an improved one-against-all multiclass support vector machines (OAA-MCSVM) classifier. Distinct feature extraction methods are simultaneously applied to an acoustic emission (AE) signal to extract unique fault features for diagnosing bearing defects. These fault features are composed of time domain, frequency domain statistical parameters, and complex envelope spectrum analysis. Generally, a high-dimensional feature vector is used to train the standard OAA-MCSVM classifier for diagnosis and identification of bearing defects. However, this classification method ignores individual classifier competence when results from multiple classes are agglomerated for the final decision, and therefore, yields undecided and overlapped feature spaces where classification accuracy is severely degraded. To solve this unreliability problem, this paper introduces a dynamic reliability measure (DReM) technique for individual support vector machines (SVMs) in the one-against-all (OAA) framework. This DReM accounts for the spatial variation of the classifier's performance by finding the local neighborhood of a test sample in the training samples space and defining a new decision function for the OAA-MCSVM. The efficacy of the proposed OAA-MCSVM classifier with DReM is tested for identifying single and multiple combined faults in low-speed bearings. The experimental results demonstrate that the proposed classifier technique is superior to three state-of-the-art algorithms, yielding 6.19–16.59% improvement in the average classification performance.

Fractal dimension-based approach for detection of multiple combined faults on induction motors

Induction motors, key elements for industry, are susceptible to one or more faults at the same time; yet, they can keep working without affecting the process, but increasing the production costs. For this reason, a monitoring system that can efficiently diagnose the induction motor condition, even under multiple combined faults, is a demanding task. In this work, a methodology and its implementation into a field programmable gate array for an online and real-time monitoring system of multiple combined faults are presented. First, the fractal dimension approach, using the Katz algorithm, is introduced as a measure of variation of 3-axis startup vibration signals for the induction motor condition, considering that these signals describe changes on its dynamic characteristics due to the different faults. Then, an artificial neural network determines in an automatic way the induction motor condition according to the fractal dimension values. The obtained results show a higher overall efficiency than previous works for detecting broken rotor bars, outer-race bearing defects, unbalance, and their combinations, as well as a healthy condition.

Fused Empirical Mode Decomposition and MUSIC Algorithms for Detecting Multiple Combined Faults in Induction Motors

Detection of failures in induction motors is one of the most important concerns in industry. An unexpected fault in the induction motors can cause a loss of financial resources and waste of time that most companies cannot afford. The contribution of this paper is a fusion of the Empirical Mode Decomposition (EMD) and Multiple Signal Classification (MUSIC) methodologies for detection of multiple combined faults which provides an accurate and effective strategy for the motor condition diagnosis.

Real-time SVD-based detection of multiple combined faults in induction motors

Early detection of induction motor faults has been a main subject of investigation for many years. Several approaches have been proposed for identifying one or more faults treated in an isolated way. Multiple combined faults on induction motors represent a big challenge since the reliable diagnosis of a faulty condition under the presence of two or more simultaneous faults is really difficult. This work introduces a novel methodology that merges singular value decomposition, statistical analysis, and artificial neural networks for multiple combined fault identification. Obtained results demonstrate its high effectiveness on detecting faulty bearings, unbalance, broken rotor bars, and all their possible combinations. The developed field programmable gate array-based implementation offers a portable low-cost solution for online classification of the rotating machine condition in real time. Thanks to its generalized nature, the introduced approach can be extended for detecting multiple combined faults under different working conditions by a proper calibration.

Smart Sensor for Online Detection of Multiple-Combined Faults in VSD-Fed Induction Motors

Induction motors fed through variable speed drives (VSD) are widely used in different industrial processes. Nowadays, the industry demands the integration of smart sensors to improve the fault detection in order to reduce cost, maintenance and power consumption. Induction motors can develop one or more faults at the same time that can be produce severe damages. The combined fault identification in induction motors is a demanding task, but it has been rarely considered in spite of being a common situation, because it is difficult to identify two or more faults simultaneously. This work presents a smart sensor for online detection of simple and multiple-combined faults in induction motors fed through a VSD in a wide frequency range covering low frequencies from 3 Hz and high frequencies up to 60 Hz based on a primary sensor being a commercially available current clamp or a hall-effect sensor. The proposed smart sensor implements a methodology based on the fast Fourier transform (FFT), RMS calculation and artificial neural networks (ANN), which are processed online using digital hardware signal processing based on field programmable gate array (FPGA).

FPGA-based entropy neural processor for online detection of multiple combined faults on induction motors

For industry, a faulty induction motor signifies production reduction and cost increase. Real-world induction motors can have one or more faults present at the same time that can mislead to a wrong decision about its operational condition. The detection of multiple combined faults is a demanding task, difficult to accomplish even with computing intensive techniques. This work introduces information entropy and artificial neural networks for detecting multiple combined faults by analyzing the 3-axis startup vibration signals of the rotating machine. A field programmable gate array implementation is developed for automatic online detection of single and combined faults in real time.

FPGA-Based Online Detection of Multiple Combined Faults in Induction Motors Through Information Entropy and Fuzzy Inference

The development of monitoring systems for rotating machines is the ability to accurately detect different faults in an incipient state. The most popular rotating machine in industry is the squirrel-cage induction motor, and the failure on such motors may have severe consequences in costs, product quality, and safety. Most of the condition-monitoring techniques for induction motors focus on a single specific fault. The identification of two or more combined faults has been rarely considered, in spite of being a very usual situation in real rotary machines. On the other hand, information entropy is a signal processing technique that has recently proved its suitability for fault detection on induction motors, and fuzzy logic analysis has extensively been used in combination with several processing techniques in improving the diagnosis of a single isolated fault. The contribution of this paper is a novel methodology that is suitable for hardware implementation, which merges information entropy analysis with fuzzy logic inference to identify faults like bearing defects, unbalance, broken rotor bars, and combinations of faults by analyzing one phase of the induction motor steady-state current signal. The proposed methodology shows satisfactory results that prove its suitability for online detection of single and multiple combined faults in an automatic way through its hardware implementation in a field programmable gate array device.

The Application of High-Resolution Spectral Analysis for Identifying Multiple Combined Faults in Induction Motors

Induction motors are critical components for most industries. Induction motor failures may yield an unexpected interruption at the industry plant. Several conventional vibration and current analysis techniques exist by which certain faults in rotating machinery can be identified; however, they generally deal with a single fault only. Instead, in real induction machines, the case of multiple faults is common. When multiple faults exist, vibration and current are excited by several fault-related frequencies combined with each other, linearly or nonlinearly. Different techniques have been proposed for the diagnosis of rotating machinery in literature, where most of them are focused on detecting single faults and few works deal with the diagnosis and identification of multiple combined faults. The contribution of this paper is the development of a condition-monitoring strategy that can make accurate and reliable assessments of the presence of specific fault conditions in induction motors with single or multiple combined faults present. The proposed method combines a finite impulse response filter bank with high-resolution spectral analysis based on multiple signal classification for an accurate identification of the frequency-related fault. Results show the methodology potentiality as a deterministic detection technique that is suited for detecting multiple features where the fault-related frequencies are very close to those analytically reported in literature.