Principal Component Analysis (PCA) based neural network for motor protection

Protection of an induction motor (IM) against possible faults, such as a stator winding fault, due to thermal deterioration, rotor bar and bearing failures, is very important in environments in which it is used intensively, as in industry as an actuator. In this work, a real time digital protection algorithm based on principal component analysis (PCA) and neural network method is presented for induction motors. The proposed protection algorithm covers internal winding faults (also known as stator faults), broken rotor bar faults, and bearing faults. Many laboratory experiments have been performed on a specially designed induction motor to evaluate the performance of the suggested protection algorithm. The hybrid protection algorithm described in this paper uses is based on a three phase rms supply. These currents are first preprocessed by PCA to extract distinctive features called residuals. The calculated residuals are applied to a feed-forward back-propagation neural network as input vectors for decision making. Outputs of the network are signals denoting winding fault, rotor bar fault, bearing fault, and normal operation. The proposed algorithm is implemented by using Matlab^{TM} and C++ with a NI-DAQ data acquisition board.

Broken rotor bar (BRB) fault is common in cage rotor induction motors. Motor current signature analysis (MCSA) has been a popular method to detect BRB faults. In the MCSA, the fault signature frequencies for BRB are close to the fundamental frequency. This spectral nearness leads to the requirement of larger observation windows, which inherently increases BRB fault detection time. This paper presents an alternative algorithm to detect BRB faults in induction motors from MCSA using two Taylor–Kalman (TK) filters in cascade with a subsampling scheme. The proposed BRB fault detection approach allows to use the TK filter to estimate lower frequencies with less computational burden in comparison to conventional TK analysis. Experimental analysis shows that nearly accurate estimates and competitive detection time can be achieved by the proposed BRB detection method. The performance of the proposed algorithm has been compared with classical spectral techniques using numerical simulations and records acquired from induction motors under real operating conditions.

Broken rotor bar (BRB) faults are one of the most common faults in induction motors (IM). One or more broken bars can reduce the performance and efficiency of the IM and hence waste the electrical power and decrease the reliability of the whole mechanical system. This paper proposes an effective fault diagnosis method using the Teager–Kaiser energy operator (TKEO) for BRB faults detection based on the motor current signal analysis (MCSA). The TKEO is investigated and applied to remove the main supply component of the motor current for accurate fault feature extraction, especially for an IM operating at low load with low slip. Through sensing the estimation of the instantaneous amplitude (IA) and instantaneous frequency (IF) of the motor current signal using TKEO, the fault characteristic frequencies can be enhanced and extracted for the accurate detection of BRB fault severities under different operating conditions. The proposed method has been validated by simulation and experimental studies that tested the IMs with different BRB fault severities to consider the effectiveness of the proposed method. The obtained results are compared with those obtained using the conventional envelope analysis methods and showed that the proposed method provides more accurate fault diagnosis results and can distinguish the BRB fault types and severities effectively, especially for operating conditions with low loads.

Three-phase induction motors are among the most frequently used motors in industrial areas due to their simple structure, low cost, power specifications, etc. Electric energy consumption from these motors accounts for 68% of the energy used by all motors. The faults that occur in these motors over time decrease motor efficiency and result in significant energy consumption. In this study, a new method based on spectral subtraction (SS) was suggested for determining broken rotor bar (BRB) faults in these motors. The traditional method of motor current signature analysis via Fast Fourier Transform (FFT) is hard to use to diagnose BRB faults because the sideband characteristics used as a fault indicator are also seen in the healthy state at low amplitude levels. In the proposed fault detection method, the FFT of both healthy case and faulty case current signals were calculated and then the SS signal is obtained by subtracting the FFT of the healthy motor from the faulty motor for each time step. In the residual SS signal, fault detection was performed by examining the amplitude levels of the harmonic component of the BRB fault. Experimental results indicate that BRB faults can be successfully detected in squirrel-cage rotor induction motors using the suggested method.

Multivariate outlier detection and remediation in geochemical databases

A critical analysis on broken rotor bar fault in three-phase induction motor is presented in this manuscript. Core research is carried on model-based broken bars fault detection and diagnosis for induction motors. A wide variety of faulty induction motor models are available in the literature. Out of which the model induction motor models based reference frames are widely used in fault diagnosis. It is believed to be a novel contribution, as researchers haven't considered whether these faulty models behave similar behaviour in stationary, rotor, synchronous and arbitrary reference frames. As an extension, a newly modified broken rotor induction motor model is proposed in this manuscript. To validate proposed idea dynamic computations of models are carried on using MATLAB R2022a programming software. The novel idea is verified with extensive simulation studies with various induction motors. The results and observations are presented clearly in this manuscript.

Ultrasound (US) imaging is the initial phase in the preliminary diagnosis for the treatment of kidney diseases, particularly to estimate kidney size, shape and position, to give information about kidney function, and to help in diagnosis of abnormalities like cysts, stones, junctional parenchyma and tumors which is shown in Figs. 7–9. This study proposes Grey Level Co-occurrence Matrix (GLCM)-based Probabilistic Principal Component Analysis (PPCA) and Artificial Neural Network (ANN) method for the classification of kidney images. Grey Wolf Optimization (GWO) is used to update the current positions of abnormal kidney images in the discrete searching space, thus getting the optimal feature subset for better classification purposes based on Feed Forward Neural Network (FFNN). The scanned image is pre-processed and the required features are extracted by GLCM, among those, some features are selected by PPCA. Feed Forward Back propagation Neural Network (FFBN) is used to classify the normalities and abnormalities in the part of kidney images. The proposed methodology is implemented in MATLAB platform and the analyzed result produces 98% accuracy using GWO-FFBN technique.

Induction machines (IMs) are commonly used in various industrial sectors. It is essential to recognize IM defects at their earliest stage so as to prevent machine performance degradation and improve production quality and safety. This work will focus on IM broken rotor bar (BRB) fault detection, as BRB fault could generate extra heating, vibration, acoustic noise, or even sparks in IMs. In this paper, a modified empirical mode decomposition (EMD) technique, or MEMD, is proposed for BRB fault detection using motor current signature analysis. A smart sensor-based data acquisition (DAQ) system is developed by our research team and is used to collect current signals wirelessly. The MEMD takes several processing steps. Firstly, correlation-based EMD analysis is undertaken to select the most representative intrinsic mode function (IMF). Secondly, an adaptive window function is suggested for spectral operation and analysis to detect the BRB fault. Thirdly, a new reference function is proposed to generate the fault index for fault severity diagnosis analytically. The effectiveness of the proposed MEMD technique is verified experimentally.

The prognosis of childhood nephrotic syndrome directly hinges on the accurate prediction of negative conversion days (NCDs). Therefore, this paper designs a hybrid approach of principal component analysis (PCA) and backpropagation (BP)-adaptive boosting (AdaBoost) neural network (NN), and applies the method to predict the NCDs for children with nephrotic syndrome. Specifically, PCA method was employed for dimension reduction. Six principal components were extracted from multiple physiological features, and taken as input variables of three-tiered neural networks. The boosted predictor of BP-AdaBoost model, together with three predictors of BP NN, support vector machine (SVM) and radial basis function (RBF) NN, was trained for NCDs prediction. The experimental results show that the predictor of BP-AdaBoost NN achieves the mean absolute error of 0.2334, the mean relative error of 3.2789%, the SD of 4.6804 and the RSD of 50.8053% in NCDs prediction, and it outperforms other predictors of BP NN, SVM and RBF NN on both accuracy and precision. Furthermore, comparison experiments are conducted on PCA processed testing data and raw testing data for BP-AdaBoost NN and demonstrate the excellent effect of PCA. The hybrid approach of PCA and BP-AdaBoost NN is simple and reliable for NCDs prediction of childhood nephrotic syndrome, and it can help pediatricians prognose childhood nephrotic syndrome accurately and further provide patients with better care and treatment.

In this paper, the performance of machine learning methods for squirrel cage induction motor broken rotor bar (BRB) fault detection is evaluated. Decision tree classification (DTC), artificial neural network (ANN), and deep learning (DL) methods are developed, applied, and studied to compare their performance in detecting broken rotor bar faults in squirrel cage induction motors. The training data were collected through experimental measurements. The BRB fault features were extracted from measured line-current signatures through a transformation from the time domain to the frequency domain using discrete Fourier Transform (DFT) of the frequency spectrum of the current signal. Eighty percent of the data were used for training the models, and twenty percent were used for testing. A confusion matrix was used to validate the models’ performance using accuracy, precision, recall, and f1-scores. The results evidence that the DTC is less load-dependent, and it has better accuracy and precision for both unloaded and loaded squirrel cage induction motors when compared with the DL and ANN methods. The DTC method achieved higher accuracy in the detection of the magnitudes of the twice-frequency sideband components induced in stator currents by BRB faults when compared with the DL and ANN methods. Although the detection accuracy and precision are higher for the loaded motor than the unloaded motor, the DTC method managed to also exhibit a high accuracy for the unloaded current when compared with the DL and ANN methods. The DTC is, therefore, a suitable candidate to detect broken rotor bar faults on trained data for lightly or thoroughly loaded squirrel cage induction motors using the characteristics of the measured line-current signature.

Detection of half broken rotor bar fault in VFD driven induction motor drive using motor square current MUSIC analysis

Mixed-fault diagnosis in induction motors considering varying load and broken bars location

This paper proposes the use of mean absolute difference (MAD) technique to perform broken rotor bar fault defection in induction motors. Feature extraction is performed on several motor current frequency components according to different load conditions in order to obtain meaningful feature vectors for broken rotor bar fault detection purposes. The MAD between the predetermined reference vector and the feature vector extracted from the input current spectrum is used to determine whether the motor has fault or not. The MAD approach is simple and can be effectively used for broken rotor fault detection, provided that appropriate feature vector for fault information is used. Experimental results show that the proposed method effectively detects the broken rotor bar faults under different load conditions.

Prediction model of end-point phosphorus content in BOF steelmaking process based on PCA and BP neural network

A novel online structure damage identification using Principal Component Analysis (PCA) techniques and the perceptron backpropagation neural network is presented. There are three phases to execute this method. In Phase I, system modal information, frequencies and mode shapes, are calculated. Phase II is for damage location identification; the Residual Force Vectors (RFVs) are computed as input to the first neural network. Then the network was trained to simulate damage location identification. Phase III is the severity identification step. The PCA method is used to modify the input for the second neural network. Then this network identifies the severity. There are three advantages of using the PCA method, First, PCA method characterizes the original modal information precisely. Second, PCA method creates the unique data for different damage cases unlike other modal property based data. Third, the accuracy of the damage identification improves significantly, when compared with previously developed methods. This method can be operated online for the real time structural damage identification.