Modelling of Technical Systems

Current signature analysis of induction motor becomes very much popular for its Condition monitoring purpose. Most of these studies in recent years are focused on hardware. Though, to build an experimental set-up for fault emulating machines is not an easy task. It involves too much cost and risk of damage. Moreover, it is not possible to create different types of faults in the same machine. In this paper an accurate but simple model of 3-phase Induction motor is being proposed which describes the machine at low frequency as well as at high frequency for different types of fault situations. The model, based on DM, CM characteristics of the machine, is well suited for power frequency (50 Hz) and high frequency applications like adjustable speed drives (ASDs). A mechanical model of a three-phase induction machine is incorporated to this electrical model to make a universal model of dynamic squirrel cage induction motor for overall simulation. The proposed model has the feature to incorporate different types of electrical fault like, stator inter-turn fault, phase-to-phase and phase-to ground insulation fault etc. The premature conditions of the above faults can also be emulated.

A combined image processing and Nearest Neighbor Algorithm tool for classification of incipient faults in induction motor drives

A general framework and a method for model based diagnosis are suggested. In the framework, faults and modeling of faults are formally described. Then the method is used to construct a diagnosis system based on a structure of hypothesis tests. Using these principles, it is possible to diagnose a large variety of different types of faults. The framework and the method are applied to the design of a diagnosis system for an automotive engine. This application clearly shows the strength of these principles because several different types of sensor faults and leakage need to be diagnosed. The resulting diagnosis system is demonstrated using experiments on a real automotive engine.

Occurrence of unwanted resistance at terminal of IGBTs is a type of Incipient faults which occurs in 3-phase voltage source Inverter. This type of faults does not trigger the protective circuit and in due course of time leads to severe faults. The present work had made an attempt to identify this type of faults related to two IGBTs in the VSI scheme. Rigorous simulation is done with different cases of faults in a PSIM software platform. The current profiles obtained from the simulation are processed by data processing tools to obtain Mean Current vector (MCV). A two phase reference frame shows the distribution of MCV for different types of faults. The distribution is found to follow a particular pattern. This has encouraged the authors to use SVM classifier for analyzing the distributed pattern and classify different types of faults.

The transient performance of Smith single phase motor supplied by the single phase SVPWM voltage source inverter is studied emphatically. And then, voltage space vector of the single phase whole-bridge inverter is analyzed and the SVPWM whose control algorithm is deduced. Meanwhile, a transient mathematical model for the Smith single phase motor is developed. Moreover, a simulation model of variable frequency speed control system of single phase SVPWM inverter-Smith single phase motor is built by MATLAB/SIMULINK. Finally, by the simulation calculated to the actual case, the result is consistent with the theoretical analysis. Thus, it would be of a certain practical significance and reference value in the design of SVPWM inverter and speed control system of tri-winding single-phase condenser motor.

Purpose The paper presents the results of work on control systems of bearingless electric motors. Authors proposed the applications of bearingless electric machines for aircraft actuation system. Suggested solution characterizes novel concept of on-board equipment design such as More Electric Aircraft. Magnetic suspension technology allows elimination of friction force and the negative performance features of classic bearing system. However, to achieve all these purposes appropriately, dedicated control system must be also applied. Design/methodology/approach The development of a control system of bearingless electric machine is presented in detail. Mathematical model and construction of induction bearingless motor are widely discussed. Then, proportional–integral-derivative controller algorithm designing for BEM control system was presented using the well pole placement method. Simulation model of BEM control system with use of Matlab-Simulink software was shown. Finally, experimental studies on laboratory stand were introduced. The paper presents design methodology of conventional and advanced control system of bearingless motor. Findings The presented concept of the bearingless electric machines could be applied in the on-board actuation system. During research, full control system of bearingless electric motor was designed and tested. This system consisted of two subsystems. The first responded for rotary speed stabilization and second one was designed for position control of the rotor in the air gap. Practical implications The presented concept of the bearingless electric machines could be applied in the on-board actuation system. During research, full control system of bearingless electric motor was designed and tested. This system consisted of two subsystems. The first responded for rotary speed stabilization and second one was designed for position control of the rotor in the air gap. Originality/value The idea of active magnetic suspension system will be implemented for aviation on technology readiness level V. The paper presents unique laboratory stand with bearingless electric motor and experimental studies. The stable time responses of designed control system were presented and discussed. In addition, preliminary considerations of advanced control system with robust controller were introduced as well.

We propose a new batch bioprocess monitoring approach which combines the Gaussian mixture model (GMM) with the multiway analysis in localized discriminant embedding subspace for detecting and classifying different types of faults. Due to the inherent process multimodality within abnormal points, the capability of traditional multimay locality preserving projection (MLPP) is degraded. For the routine bioprocess operation with abnormal events, GMM is used to partition the training set with various types of faults into different clusters. Three localized distance matrices reflecting the relationships of nearby points are then computed. The extracted leading discriminant embedding directions can not only separate the normal and faulty data by maximizing the distance among nearby data points from different clusters, but also preserve the intrinsic geometrical structure within the multiple faulty clusters by minimizing the small distance and penalizing the large distance among neighboring data from the same cluster. The newly developed multiway localized discriminant embedding (MLDE) approach is applied to two test scenarios in the fed-batch penicillin fermentation process and compared with the conventional MLPP method. The results demonstrate that the MLDE approach is superior to the MFDA method in detecting abnormal events and classifying different types of faults in fed-batch process with higher accuracy and sensitivity.

Internal winding faults are among the most common causes of transformer failure. Once a fault occurs, a fast and efficient method for its detection and location is required to repair and reconnect the device, thus avoiding further delays in the network operation. This paper introduces a simple method for the location of internal winding faults. This method is based on time-domain terminal measurements of wave propagation along the winding. By means of low-cost laboratory components (a lowvoltage DC source and an oscilloscope), different types of faults in layer-type windings can be detected and located with high accuracy. A frequency-domain distributed-parameter winding model is used to predict the transient response of the winding subjected to different types of faults. FEM simulations are used to compute the model parameters. A test case is presented to demonstrate the efficacy of the fault location method.

Current methods for processing acoustic signals for fault diagnosis are commonly based on the method of processing the vibration signals. It is difficult to obtain the information only existing in the acoustic signal. Therefore, considering the fault diagnosis based on the acoustic signal, a method based on the Logarithmic Mel (LM) spectrogram and a residual neural network (ResNet) is proposed. Firstly, to correspond with the auditory perception, the acoustic signal is processed and linearly represented in the LM spectrogram. Secondly, the ResNet model is introduced to extract the fault information from the LM spectrogram for the identification of the different types of faults. By training two datasets obtained from the gearbox platform, the different types of faults and the faults at different components of the gearbox are separately classified. The comparison results of different inputs with the LM spectrogram for training the ResNet model show that the LM spectrogram as the input could achieve the best classification accuracy. The comparison results of different classic deep learning models with the ResNet model demonstrate that the proposed methods can effectively extract features from acoustic signals in the LM spectrogram for the fault classification of the gearbox.

Brushless DC (BLDC) motors are used extensively in the industry for a wide variety of applications. In the Aerospace industry, they are mainly used in the control actuation system in launch vehicles. Different types of faults can develop in a BLDC motor. It is essential to detect these faults in the early stage itself before they develop into system failures. In this paper, fault detection in BLDC motors using parameter estimation technique is presented. Detailed fault diagnosis is subsequently carried out by a fuzzy inference system. Discrete Square Root Filtering in Covariance form (DSFC) algorithm is used initially for estimating the physical parameters of the BLDC motor. These parameters are then passed on to the fuzzy inference system which carries out the detailed diagnosis. Simulation studies carried out indicates that the developed fault detection and isolation scheme is capable of isolating different types of faults like open circuit, short circuit faults, performance degradation of magnets etc. in BLDC motors.

This paper presents an interpretable machine learning approach to classify different types of faults in ball bearing systems with high accuracy. These methods are based on ensemble methods (Random Forest, Gradient Boosting, and AdaBoost) which use data from the fan system collected by vibration monitors mounted on three axes. The proposed technique needs statistical feature inputs to classify different types of faults. We train models and explain their output using SHAP (SHapley Additive exPlanations). In the rigorous numerical study, we point out a suitable explanation for black-box predictive models trained on vibration data. Our model achieves 98 percent classification accuracy when classifying all fault types.

The emergence of Cloud Computing has brought new dimension to the world of information technology. Even though Cloud Computing provide various benefits like agility, on-demand provisioning of resources, reduced cost, multi-tenancy etc., there are risks and flaws associated with it. One key research challenge in Cloud Computing is to ensure continuous reliability and guaranteed availability of resources provided by it. So there is a need for a robust Fault Tolerant (FT) system in Cloud Computing. To better understand FT in Cloud Computing, it is essential to understand the different types of faults. In this paper, we highlight the basic concepts of fault tolerance by understanding the different FT policies like Reactive FT policy and Proactive FT policy and the associated FT techniques used on different types of faults. A study on various fault tolerant methods, algorithms, frameworks etc., has been carried out which are developed and implemented by research experts in this field. This is an area where lot of research is happening and these studies will guide us to build a robust FT technique in Cloud.

Consumption of electrical energy has increased many folds in the recent time due to its use for industrial, commercial, agricultural, domestic and social purposes. HVDC technology is increasingly used for bulk power transmission due to advantages like low loss, ease of control, etc. However, the protection system for the HVDC system is not as mature as AC system. HVDC system requires fast and accurate fault detection. Recently, various time- and frequency-domain algorithms are proposed. In this paper, Stockwell (S-) transform is utilized for real-time fault detection. Different type of faults in HVDC transmission line are simulated using PSCAD. The generated fault signals are analyzed using MATLAB. S-transform can detect the transient signal effectively in time-frequency domain. It can be used to robustly differentiate the different type of faults and the load variation.

The proposed technique consists of a pre-processing unit based on Continuous Wavelet Transform (CWT) in combination with an Artificial Neural Networks (ANN). CWT acts as an extractor of distinctive features in the transient current signals at the sending and receiving end of the transmission line. This information is then fed to the ANN for detecting the fault type and location of fault. The results presented clearly indicate that the present technique is very fast, computationally efficient and intelligent enough to accurately identify three different types of fault (LG & LLG) and their locations.

Electric motor drives with a wide variety of applications are usually derived with inverters, where the inverter switches are always prone to different types of faults. Short circuit faults can rapidly shut down systems, and open-circuit ones can lead to secondary damage if they are not detected and tolerated in time. Due to this fact, in this paper, a novel data-driven fault detection and diagnosis (FDD) method has been proposed to detect and locate all types of inverter switch faults. Three deep learning algorithms, including fully connected neural networks (FCNs), convolutional neural networks (CNNs), and bidirectional long short-term memory (BiLSTM), have been implemented and compared. The BiLSTM network with 98.45% accuracy outperforms the others and can detect all types of faults in less than half a fundamental period under different and variable speeds with the existence of noise. The results show that the proposed method is highly effective and is a great candidate for real-time applications.