An efficient fault diagnosis model using Lappet Falco optimisation based on a deep neural network for the VSI under varying load conditions

Induction Machine (IM) fault detection techniques such as Fast Fourier Transform (FFT) which is a popular steady-state analysis method is recognized to be highly dependent on the IM loading and speed conditions. Nonetheless, implementing an FFT or even Short Time Fourier Transform (STFT) will result in low resolution frequency characteristics especially under a variable speed and loading conditions. Consequently, fault detection and classification under variable loading and speed conditions is quite inconvenient. Since, mechanical faults are one of the major breakdowns, which occur in IMs, it needs to be addressed to prevent breakage andfault extension. This paper investigates and detects faults under variable loading and speed conditions by studying the Motor Current Signature Analysis (MCSA) using a novel developed parallel technique based on the discrete wavelet transform (DWT).The proposed model input would be MCSA with the similar drive, loading condition and constraints for both healthy and faulty electric motors and the Discrete Wavelet Transform (DWT) is used as a detection technique. The proposed technique uses the DWT to the IM’s stator current to extract the desired features including Min, Max, Standard deviation, and Energy of the signal as a specific vector for each electric motor. In addition, different mechanical faults including Rotor broken bar(s), eccentricity and bearing have been detected using the proposed technique. Then, the accuracy of proposed parallel model using DWT is verified using experimental set-up of the parallel machines under variable loading and speed conditions for different mechanical faults. Finally the results of the proposed technique for all mechanical faults is tabulated and presented.

Generally, the design of the transmission elements is made by considering the worst weather and load conditions in the power system. When the temperature of the cables and overhead lines is low, these can have a capacity of loading more. This situation causes the lines to design based on static rating calculation to be used inefficiently. Therefore, operational flexibility of power system can be achieved by using dynamic rating methods in overhead lines and underground cables, while the variable weather conditions and loading conditions are considered. Unlike the studies in the literature, the thermal analysis of the cable can be made by considering the changing loading and weather conditions. In the analysis, the wind power plants is discussed for variable loading conditions. As a result of this assumption, it is found that the variable loading has a very important effect on the dynamic line rating, such as weather conditions. The economic consequences of variable load conditions on cable sizing are analyzed using the net present value method by considering the dynamic rating calculations. In the analysis, it is revealed that if the dynamic rating of the cable is used, it will be suitable for short-term investments. In this study, thermal analysis of underground cables is based on numerical method by using Fluent program, while overhead lines are based on analytical method by using the ETAP program.

Most of the conventional tracking techniques fail to track maximum power point under partially shaded conditions as the photovoltaic (PV) array characteristic curves exhibit multiple local maxima. This paper put forward an adaptive maximum power point tracking technique (MPPT) for PV array which can perform even in non-uniform irradiance conditions. The proposed method targets on extracting the power of non-shaded PV cells during partial shading condition with the help of forward biased bypass diode across the shaded PV cells. The proposed technique implements Mamdani fuzzy inference system based fuzzy logic to control which is further equipped with DC-DC boost converters. The proposed technique enables the PV array to operate efficiently at variable load and non-uniform irradiance condition. The effectiveness of the proposed MPPT technique is investigated for various partial shaded patterns and variable load conditions and the results are compared with the modified perturb & observe MPPT technique.

DC-DC converters are widely used in the switched mode power supplies for its high conversion efficiency and flexible output voltage. Regulation problems of the DC-DC converters under variable load and parametric condition lead to the development of sliding mode controller. In this paper adaptive and non-adaptive sliding mode controllers are implemented to control the output voltage of buck converter under variable load conditions. A suitable sliding coefficient is designed by deriving the state space representation of the buck converter and modified it to be adaptive to the loading conditions. It is observed that with the adaptive sliding mode controller, the output voltage not only remained constant for step overload but also reached the desired value faster. Adaptive sliding mode controller also saved the buck converter from entering into discontinuous mode under step under load condition.

DC-DC converters are widely used in the switched mode power supplies for its high conversion efficiency and flexible output voltage. Regulation problems of the DC-DC converters under variable load and parametric condition lead to the development of sliding mode controller. In this paper adaptive and non-adaptive sliding mode controllers are implemented to control the output voltage of buck converter under variable load conditions. A suitable sliding coefficient is designed by deriving the state space representation of the buck converter and modified it to be adaptive to the loading conditions. It is observed that with the adaptive sliding mode controller, the output voltage not only remained constant for step overload but also reached the desired value faster. Adaptive sliding mode controller also saved the buck converter from entering into discontinuous mode under step under load condition.

Objectives: The main objective of this work is to obtain input power factor closer to unity and regulation of output voltage of semi bridgeless AC-DC converter using linear peak current mode control. Methods/Statistical Analysis: Linear Peak Current Mode control technique is applied to Semi-Bridgeless AC-DC Converter in this paper. This technique helps in achieving power factor closer to unity. The output voltage of the converter is regulated at 200V. The prototype is designed for 200W. Simulation results are obtained for 200W using PSIM software(Simulation Software Package) the converter is analyzed under variable load and supply conditions. Findings: At variable load conditions, power factor is maintained closer to unity and output voltage is regulated to 200V. The efficiency for the converter is found to 96% at full load conditions. Application: Battery charging applications.

This study aims to determine the effect of high temperature on the fatigue life of AA7075-Al2O3 nanocomposites (6 wt % Al2O3) fabricated by stir casting. The research problem is to determine the durability, fatigue resistance, and mechanical properties of the nanocomposite under constant and variable loading conditions at elevated temperatures, as well as to identify changes in its behavior due to exposure to high temperatures. The results show that higher temperatures have a big effect on the nanocomposite's fatigue performance under both loading conditions. When the material was tested at a high temperature (150 °C) with an extra 6 wt % Al2O3, the ultimate tensile strength and yield stress both went up by 16 % and 15.7 %, respectively. Its fatigue life was also successfully tested under both variable and constant amplitude load conditions. The interpretation of the results suggests that the changes in the microstructure of the nanocomposite material at elevated temperatures lead to an increase in dislocation density and grain size, resulting in an improvement in its mechanical properties. The findings can be utilized to optimize the nanocomposite fabrication process and enhance its fatigue resistance at high temperatures. In addition, the results can be used to enhance the design of aerospace components and high-temperature engines that require materials with excellent fatigue resistance at elevated temperatures. In summary, the investigation of the effect of high temperature on the constant and variable fatigue lives of AA7075-Al2O3 nanocomposite provides valuable insight into the material's mechanical properties. The findings contribute to the development of materials that can withstand high-temperature conditions, which has implications for a variety of industries.

Track and hunt metaheuristic based deep neural network based fault diagnosis model for the voltage source inverter under varying load conditions

Recently, graph convolution network (GCN) has been the focus in fault diagnosis for its powerful representational ability in relationship mining. However, with the difficulty in extracting the weak features of the signal under variable load conditions, GCN is not suitable for deep neural network (DNN), and the receptive scale of GCN is unknown that limits the application of GCN in machine fault diagnosis. To address these issues, a multi-scale cluster-graph convolution neural network with multi-channel residual network (MR-MCGCN) is proposed for machine fault diagnosis in this article. First, multi-channel residual network (MCRN) is proposed for extracting the weak feature in the signal. Then, the finite graph data of signal and different scales are generated by the autoencoder (AE) graph generation layer. Finally, a multi-scale cluster-graph convolution neural network is proposed for achieving intelligent fault diagnosis. Also, the three different datasets are used for verifying the effectiveness of the proposed MR-MCGCN. The experimental results show that the proposed MR-MCGNN can achieve the highest diagnosis results than other methods even under variable load conditions.

An efficient fault diagnosis model using Lappet Falco optimisation based on a deep neural network for the VSI under varying load conditions

Neural Network Fault Diagnosis of Voltage Source Inverter under variable load conditions at different frequencies

Under variable load conditions, the bearing vibration signal is non-stationary, which renders ineffective the techniques used for bearing fault diagnosis under constant running conditions. A fault diagnosis model of the variational mode decomposition (VMD) and multi-classification correlation vector machine (MRVM) based on chaotic quantum particle swarm optimization (CQPSO) is proposed. First, the number of intrinsic mode function (IMF) and penalty parameter of VMD is optimized by QPSO algorithm to search the optimal combination value of two parameters. Then, the optimal combination of parameter values corresponding to the parameters of VMD algorithm are set, and decompose the known fault signal. The two dimensional marginal spectral entropy of the IMF component is used as the input eigenvector of the multi-classification RVM. Finally, the experimental data under variable load conditions are used to verify the method. The experimental results show that the proposed method can accurately diagnose the type and degree of the bearing fault in the variable load condition with high diagnostic accuracy and strong robustness.

The fault diagnosis of rolling bearings is a critical aspect of rotating machinery, as it significantly contributes to the overall operational safety of the mechanical equipment. In the practical engineering environment, the complex and variable working conditions, along with the presence of overlapping noise, contribute to intricate frequency information in the acquired signals and their highly time-dependent characteristics, which makes it difficult to extract the available fault features hidden in the signal. Based on this, a hybrid fault diagnosis method named GGRU-1DCNN-AdaBN is introduced, which combines improved gap-gated recurrent unit network (GGRU), one-dimensional convolutional neural network (1DCNN), and adaptive batch normalization (AdaBN). The proposed approach involves several parts to enhance fault diagnosis accuracy in vibration signals under constant load conditions and variable load conditions. Firstly, the end-layer structure of the traditional GRU is replaced with a one-dimensional global average pooling layer to aggregate the influence components of defects and reduce model training parameters. Secondly, the fusion of different types of frequency and sequence features is achieved by combining 1DCNN, addressing the limitation of a single network’s feature extraction capability and the loss of temporal features in a cascaded hybrid model. Subsequently, the fused features are input into a softmax multi-classifier to obtain fault type identification results. Lastly, the GGRU-1DCNN method is further improved by incorporating the AdaBN algorithm, enhancing the model’s domain adaptive capability under variable load conditions and noisy environments. The method is validated using datasets obtained from Case Western Reserve University, aero-engine bearings, Xi’an Jiaotong University, and the Changxing Sumyoung Technology. The findings suggest that the proposed method demonstrates superior accuracy and robustness in fault diagnosis, as well as excellent generalization capability and universal applicability.

To clarify the effective factors for the crack propagation under varying load conditions, crack propagation is investigated using V-notched specimens and radial holed ones of 0.55% carbon steel and the following results are obtained. (1) The most important effective factor for the crack propagation under high-low type varying load condition is the essential increase of the stress at the crack tip for the opening of cracked surface caused by the repeated compressive plastic deformation. (2) In the case of steel specimens, there is no effect for the crack propagation under varying load condition caused by structural changing and it is also possible to estimate the crack propagation quantitatively considering the effect of crack opening. (3) It is also possible to estimate almost all the experimental results under varying load conditions qualitatively considering the effect of crack opening and existence of incubation period. (4) The effect of crack opening appears in the case of reversible stress condition but does not appear in the case of pulsating tension stress condition. This can be predicted by the calculation of the actual stress at the crack tip under varying load conditions.

The open-circuit fault diagnosis of switching devices in 3ϕ V/F-controlled voltage source inverters is critical, since diagnostic parameters change with varying load conditions and frequency. Park’s vector transform-based approaches depend on threshold values for fault diagnosis, demanding continuous modifications based on load variations, making them prone to improper diagnosis. Artificial intelligence-based methods give good accuracy, but they require extensive data collection under varying load conditions, creating implementation efforts that are considerably high. This paper focuses on optimizing threshold-independent methods and reducing data requirements for the artificial intelligence-based open-circuit fault diagnosis of 3ϕ V/F-controlled VSIs. To mitigate the problem of fault misclassification under variable load conditions at different frequencies, the stator current is normalized using Park’s vector transform. Normalized currents ensure that the extracted features remain the same under all load conditions while providing distinctive features for faulty and healthy conditions. Feature extraction is implemented using the wavelet transform, and feature selection is carried out using a ReliefF algorithm, which enhances classification by selecting key features. The selected features are then used to diagnose faults using an extreme gradient boosting algorithm. In XGBoost, a random search is preferred over a grid search to find the best hyperparameters for optimal performance, as it speeds up tuning, explores more options, and efficiently balances accuracy. The proposed system outperforms current open-switch fault diagnosis approaches by providing high effectiveness, strong resistivity, and a fast detection time. The results are presented for different combinations of single and multiple open-switch faults under variable load conditions at different frequencies.