An improved signal processing-based fault detection technique for induction machine drives

Fault detection in induction machines has become a topic of great interest for researchers due to the growing utility these are giving to today’s industry. Real time machine learning methods are recently proposed in this area to improve the precision of fault detection. In this paper, a novel methodology based on texture feature estimation using an artificial neural network is proposed to be used for fault detection of induction machines. The method is tested for a typical fault as rotor phase opening of a wound rotor induction machine. For this, the stator current in time domain is used to detect the fault based on the proposed method. The experimental results are shown for several loads and speeds conditions to prove the effectiveness of the method.

This paper presents the analysis and development of a new method for electric fault detection in induction motor drives. The proposed method is based on extracting the magnitudes and phases of the high-frequency subband (HFSB) contents present in the d - q-axis components of the stator currents (i <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> and i <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> ) in an induction motor. The desired magnitudes and phases are extracted by processing id and iq using phaselet frames that are realized by a modulated filter bank. This filter bank is designed using six digital high-pass filters, whose coefficients are determined by biorthogonal phaselet basis functions. Extracted HFSB contents provide signature information that can offer accurate and fast detection of faults. The phaselet-based electric fault detection method has been made into a procedure for digital implementation. The performance of the proposed method is evaluated offline for collected stator currents from two different induction motor drives under different operating conditions. Offline test results show accurate, reliable, and fast detection of electric faults, with minor sensitivity to the ratings, loading levels, and type and location of faults.

An interturn short-circuit in the stator windings can lead to the breakdown of electrical machines. In the case of induction machines, several fault detection methods and faulted models have been developed in the recent decades. These models differ mainly in how the leakage inductances of the faulted winding are modeled. This work provides a generalized model for interturn short-circuit faults, using different assumptions for the leakage inductances. The model is validated with experimental results for an exhaustive set of fault parameters, and the leakage inductances influence is analyzed. Moreover, the model is used to analyze the drawbacks of the negative-sequence fundamental current as a traditional fault signature. A high-frequency injection method for converter-fed machines is presented to overcome these limits. The proposed fault signature is the negative-sequence current at the injection frequency and it is evaluated experimentally at different operating conditions. The fault severity and its location are proved to be related to the proposed fault signature.

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.

This study proposes a method for stator fault detection of inverter-fed induction machine in closed-loop control. The sideband response of inverter switching harmonics in common-mode voltage (CMV) is employed to detect the stator inter-turn fault, thus no additional injected signal is necessary. However, the PWM nature harmonics introduce undesired CMV signals, depending on the inherent imbalance or resistance asymmetry. For this reason, the CMV amplitude is not suitable to indicate the fault directly. In this study, the behaviour of switching harmonic is analysed first, and then a simple but efficient stator fault model is demonstrated. To suppress the adverse interference from inverter, an inter-turn fault indicator is proposed by utilising both of the switching sidebands. This method can obtain fault location and severity with low sensitivity to steady operating condition and stator imbalance. The experimental results validate the effectiveness of the proposed method.

In this paper, a simple and fast open-phase fault (OPF) detection and fault tolerant control method is proposed for three-phase induction machine. Instead of using integrators or low pass filter, this method utilizes the phase current magnitude and trajectory to detect open phase fault in three-phase motor drive. This significantly increases the speed of fault detection, allowing an OPF to be detected and compensated within several sampling cycles, which is advantageous for safety-critical applications. In addition, a double synchronous reference frame PI (DSRF-PI) controller method is adopted as the fault tolerant controller. Compared to previous methods that utilizes feedforward voltage compensation, the DSRF-PI does not require prior knowledge on the machine parameters and operating condition, and therefore is easier to implement. The proposed fault detection and fault tolerant control method are simulated using MATLAB Simulink simulation where the results confirms the high speed and robustness of the proposed methods.

Stator and rotor winding damages in rotating machines are result of electrical, mechanical, and thermal stress. Online magnetic field monitoring via permanently installed measuring coils inside air gap is a well-established methodology which enables winding fault detection. The paper deals with a new method for detection of stator and rotor winding inter-turn short circuits of synchronous machines and slip rings induction machines, as well as rupture of rotor bars and cage ring of induction machines. The method novelty is based on differential measurement of magnetic field by using two serial connected measuring coils. They are installed on the places (stator or rotor teeth) in the machine which have, by absolute value, equal magnetic vector potential. The distance between the measuring coils is n·tp, where tp is a pole pitch, and n = 1, 2, 3, 4,... is a multiple of the pole pitch. Measuring the coil-induced voltage enables us to detect stator and rotor winding faults, which means that measured voltage is approximately zero without fault and increases in the presence of fault. Analysis of the measuring signal allows us to detect and locate fault. With this new method it is possible with high sensitivity to determine winding fault, which enables more reliable fault detection. For example, in comparison with the motor current signature analysis method (the most widely used method for motor faults detection), this new method gives 200 times higher sensitivity to fault occurrence. Also, by using the DMFM method, faults can be detected in the time domain and there is no need for spectral or other complex signal analysis. This is very important because the measuring equipment used for machine fault detection can be simple and more economically acceptable. The DMFM method enables fault detection for even small machines with small expense in a very effective way. The only downside of the DMFM method is the fact that machine should be disassembled in order to install measuring coils. This problem is solved during the machine overhaul or during the manufacturing of the machine, when sensors can be easily implemented in the machine. For machines with large air gap, measuring coils can be installed without a machine disassembly. For the purpose of the method testing, numerous finite-element (FE) simulations on the 2- and 3-D machine models have been carried out to verify the method. Powerful numerical tools generate realistic results with properly selected starting and boundary conditions. By FEM models, actual machines with embedded measuring coils where created and simulated. The voltage induced inside the measuring coils is calculated for different machine states, load point and with and without a fault (broken rotor bar or inter-turn short circuit). Also, this method was experimentally validated via series of laboratory tests performed on the real electric machines specially designed for fault study (broken rotor bars, broken ring and inter-turn short circuits in a stator and rotor winding). Additionally, this method is applied on more than 20 real machines in industry. Due to the large amount of measured data, in this paper, it will be presented only one measurement performed on an induction motor on which we have detected one broken rotor bar. The thickness of the measuring coil designed in the printed circuit board technique is 0.3 mm. The number of turns is from 3 to 10. This new method and performed FEM calculations together with the experimental measurements improve fault detection portfolio knowledge that can be used in monitoring and diagnostics of rotating machines. Furthermore, this patent-pending method is already implemented in three innovative products placed on market (expert monitoring systems), so this method is fully confirmed in practice.

Software fault detection is crucial for ensuring reliable and high-quality software systems. However, traditional fault detection methods often rely on manual inspection or rule-based techniques, which are time-consuming and prone to human errors. In this research, the researchers propose an enhanced fault detection approach using an adaptive neural transfer learning algorithm. The goal is to leverage the power of neural networks and adaptability to improve fault detection accuracy and classification performance. The problem addressed in this research is the need for more effective fault detection methods that can handle the complexities of modern software systems. Existing fault detection techniques lack adaptability and struggle to cope with diverse software scenarios. Neural networks have shown promise in pattern recognition and classification tasks, making them suitable for fault detection. However, fixed architectures and training strategy limit their performance in different software contexts. To address this problem, the research proposes an adaptive neural transfer learning algorithm for fault detection. The algorithm dynamically adjusts its neural network architecture and training process based on the characteristics of the software under test. It incorporates adaptive mechanisms, such as adjusting learning rates and regularization techniques, to optimize performance. Real-time feedback and performance evaluation during the training process drive the adaptive mechanisms. To evaluate the proposed approach, the researchers conducted a series of experiments using diverse software systems and fault scenarios. The research compared the performance of the adaptive algorithm with traditional fault detection methods, including rule-based techniques and fixed neural network architectures. Evaluation metrics such as accuracy, precision, recall, and F1 score were used. The results consistently show that the adaptive neural transfer learning algorithm outperforms existing methods, achieving higher fault detection accuracy and improved classification performance.

ABSTRACTFaults in power transmission systems pose significant challenges due to the complexity and length of transmission lines. Effective fault detection, classification and location are essential for preventing further damage to the power grid. While travelling wave‐based algorithms are commonly used for fault location, they often focus on identifying the fault's location without classifying the fault type. Accurate classification is crucial for enabling efficient and timely responses from protection systems. This paper introduces an integrated model for fault detection, classification and location using voltage signals from a single terminal of a series‐compensated transmission line with a static synchronous series compensator (SSSC). The Gabor Transform (GT) is utilised for feature extraction, enabling both fault detection and classification. Travelling wave theory is then applied to identify the faulty segment and estimate the fault location. Additionally, a novel technique adaptively calculates the threshold value during the protection algorithm's execution. The proposed method is validated through a comprehensive analysis of various fault scenarios and sensitivity analysis. Numerical simulations in MATLAB/Simulink show that the model achieves 100% accuracy for fault detection, classification, and faulty segment identification, with 99.7925% accuracy for fault location estimation, demonstrating its effectiveness in fault management.

The day-by-day increase in digital loads draws attention towards the need for an efficient and compatible distribution network. An LVDC distribution network has the capability to fulfill such digital load demands. However, the major challenge of an LVDC distribution network is its vulnerability during a fault. The need for a high-speed fault detection method is inevitable before it can be widely adopted. This paper proposes a new fault detection method which extracts the features of the current during a fault. The proposed fault detection method uses the merits of overcurrent, the first and second derivative of current, and signal processing techniques. Three different features are extracted from a time domain current signal through a sliding window. The extracted features are based upon the root squared zero, second, and fourth order moments. The features are then set with individual thresholds to discriminate low-, high-, and very high-resistance faults. Furthermore, a fault is located through the superimposed power flow. Moreover, this study proposes a new method based on the vector sum of positive and negative pole currents to identify the faulty pole. The proposed scheme is verified by using a modified IEEE 13 node distribution network, which is implemented in Matlab/Simulink. The simulation results confirm the effectiveness of the proposed fault detection and identification method. The simulation results also confirm that a fault having a resistance of 1 m Ω is detected and interrupted within 250 μ s for the test system used in this study.

The increasing importance of power electronic converters in supplying electrical energy to utility grids places a higher priority to detect and protect against fault conditions. Fault detection and isolation are particularly important for inverters that provide black-start recovery for microgrids since these converters provide the energy source for restoration after a power outage. This paper presents a new fault detection and location method for Cascaded B-Bridge (CHB) multilevel inverters. The new fault detection method is based on monitoring the output voltage of each cell and output current directions along with each switch's state. By monitoring each cell's output voltage and current direction, the faulty cell can be detected and isolated. After the faulty cell is detected, the faulty switch can be located by comparing the current direction with the switching states. This technique is implemented with Level-Shifted Pulse Width Modulation (LS-PWM) in order to maintain acceptable total harmonic distortion (THD) levels at the converter. The proposed method can be implemented for a CHB with any number of cells, can operate with nonlinear loads, and offers very fast detection times. Simulation and experimental results verify the performance of this method.

Motor current signature analysis has been successfully used in induction machines for fault diagnosis. The method however does not always achieve good results when the load torque is not constant. This paper proposes a new approach to motor fault detection, by wavelet decomposition analysis. Theoretical development and experimental results are presented to support the research.

A data-based technique for monitoring of wound rotor induction machines: A simulation study

This article presents a novel algorithm to find optimal sets of Phasor Measurement Units (PMUs) in power systems using measurement sensitivity analysis aiming for fault detection without multi-estimation. The algorithm generalizes the impedance method in fault detection through optimizing PMU utilization in order to detect a fault with desired precision in interconnected power systems. By deriving bus voltage and currents sensitivity indices to the fault location and impedance, possible deviations of the estimated fault location and/or impedance due to measurement noise, accuracy, precision limits, or simply the inability of a measurement point to sense a fault is evaluated. Therefore, the algorithm can solve Optimal PMU Placement (OPP) for desired fault detection precision based on these indices for various points of measurement observing faults in the system. Finally, avoiding multi-estimation guarantees the unique mapping between measurements of the selected PMU sets and faults throughout the system. The proposed algorithm is performed on the IEEE 7-bus and 14-bus benchmark systems and the fault location capability is evaluated through neural networks.

Fast and selective DC fault detection and location schemes are needed for fault protection of DC Grids. In this paper, the frequency domain characteristics of DC voltage besides the current limiting reactors are analyzed and used to put forward a new fault detection method for DC grids. Hilbert-Huang transform is applied on the DC voltage waves to extract the transient frequency and transient amplitude of the DC voltage. The influence of fault type, fault distance and fault resistance of DC faults and the noise on the frequency domain transient characteristics on voltage waves are then studied. Support vector machine learns the mapping between the transient frequency and transient amplitude of the DC voltage and desired outputs, fault type and fault location through training process. Then, a fast and accurate fault detection scheme based on frequency-domain characteristics of DC voltage is proposed by SVM for DC fault detection and location in MTDC systems. This fault detection scheme requires no telecommunication, has anti-interference ability and good sensitivity and selectivity for detection of long-distance fault and high resistance grounding fault. Based on the four-terminal HVDC system built by PSCAD/EMTDC, simulations under different fault conditions are carried out to verify the effectiveness of the proposed DC fault detection scheme.