Artificial Intelligence-Based Technique for Fault Detection and Diagnosis of EV Motors: A Review

Lang, Wangjie; Hu, Yihua; Gong, Chao; Zhang, Xiaotian; Xu, Hui; Deng, Jiamei

doi:10.1109/tte.2021.3110318

Cited by 77 publications

(28 citation statements)

References 134 publications

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“…Notably, data-based prognosis has attracted a great deal of attention. In recent years, the data-based approach has gained popularity due to its high practicality, as it incorporates artificial intelligence (Lang et al, 2021 [ 25 ]).…”

Section: Related Workmentioning

confidence: 99%

Machine Learning for the Detection and Diagnosis of Anomalies in Applications Driven by Electric Motors

Ferraz Júnior,

Romero,

Hsieh

2023

Sensors

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Manufacturing systems are becoming increasingly flexible, necessitating the adoption of new technologies that allow adaptations to a turbulent and complex modern market. Consequently, modern concepts of production systems require horizontal and vertical integration, extending across value networks and within a factory or production shop. The integration of these environments enables the acquisition of a substantial amount of data containing information pertaining to production, processes, and equipment located on the shop floor. When these data and information are processed and analyzed, they have the potential to reveal valuable insights and knowledge about the manufacturing systems, offering interpretive outcomes for strategic decision making. One of the opportunities presented in this context includes the implementation of predictive maintenance (PdM). However, industrial adoption of PdM is still relatively low. In this paper, the aim is to propose a methodology for selecting the main attributes (variables) to be considered in the instrumentation setup of rotating machines driven by electric motors to decrease the associated costs and the time spent defining them. For this, the most well-known data science and machine learning algorithms are investigated to choose the one most adequate for this task. For the experiments, different testing scenarios were proposed to detect the different possible types of anomalies, such as uncoupled, overloaded, unbalanced, misaligned, and normal. The results obtained show how these algorithms can be effective in classifying the different types of anomalies and that the two models that presented the best accuracy values were k-nearest neighbor and multi-layer perceptron.

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Section: Related Workmentioning

confidence: 99%

Machine Learning for the Detection and Diagnosis of Anomalies in Applications Driven by Electric Motors

Ferraz Júnior,

Romero,

Hsieh

2023

Sensors

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“…The proportions of the step are one of the most crucial parameters determined by varying the learning rate, which in the case of setting too small a value can lead to slow convergence and the opposite of that can exceed the optimal value [71,72]. In line with to [73,74], the hyperparameters in Table 9 are as follows. The validation set is a proportion of training data used for early stopping defined with validation_fraction.…”

Section: Stochastic Gradient Descent Regressormentioning

confidence: 99%

Estimation of Excitation Current of a Synchronous Machine Using Machine Learning Methods

et al. 2022

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A synchronous machine is an electro-mechanical converter consisting of a stator and a rotor. The stator is the stationary part of a synchronous machine that is made of phase-shifted armature windings in which voltage is generated and the rotor is the rotating part made using permanent magnets or electromagnets. The excitation current is a significant parameter of the synchronous machine, and it is of immense importance to continuously monitor possible value changes to ensure the smooth and high-quality operation of the synchronous machine itself. The purpose of this paper is to estimate the excitation current on a publicly available dataset, using the following input parameters: Iy: load current; PF: power factor; e: power factor error; and df: changing of excitation current of synchronous machine, using artificial intelligence algorithms. The algorithms used in this research were: k-nearest neighbors, linear, random forest, ridge, stochastic gradient descent, support vector regressor, multi-layer perceptron, and extreme gradient boost regressor, where the worst result was elasticnet, with R2 = −0.0001, MSE = 0.0297, and MAPE = 0.1442; the best results were provided by extreme boosting regressor, with R2¯ = 0.9963, MSE¯ = 0.0001, and MAPE¯ = 0.0057, respectively.

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“…When the load of the motor does not vary according to a particular law, the effect of this method on other faults of the motor is also to be verified. Lang et al [3] introduced in detail the application of traditional machine learning and deep learning algorithms in motor fault classification. In the deep learning section, practically all algorithms are developed and modified based on artificial NNs.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis in asynchronous motors based on an optimal deep bidirectional long short-term memory networks

Xu,

Li,

Liu

et al. 2023

Meas. Sci. Technol.

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Fault diagnosis of asynchronous motors has become a pressing need in the metallurgical industry. Due to the complex structure of asynchronous motors, fault types and fault characteristics are diverse, with strong nonlinear relationships between them, which leads to the difficulty of fault diagnosis. To efficiently and accurately diagnose various motor faults, we propose a fault diagnosis method based on an optimal deep bidirectional long short-term memory neural network. First, the three-phase current, multidimensional vibrational signal, and acoustic signal of the asynchronous motor are collected and construct diverse and robust data sample set to enhance the generalization ability of the model. Next, a modified 3D logistics-sine complex chaotic map (3D LSCCM) is constructed to improve the global and local search capabilities of the pigeon swarm optimization algorithm (PIO). Then, we construct a deep bidirectional long short-term memory network (Bid-LSTM) with attention mechanism to mine high-value fault characteristic information. Meanwhile, the optimal hyper-parameters of the deep ABid-LSTM are explored using the modified PIO to improve the training performance of the model. Finally, the fault data samples of asynchronous motor are induced to train and test the proposed framework. By fusing diverse data samples, the proposed method outperforms conventional deep Bid-LSTM and achieves fault diagnosis accuracy of 99.13%. It provides a novel diagnostic strategy for motor fault diagnosis.

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Artificial Intelligence-Based Technique for Fault Detection and Diagnosis of EV Motors: A Review

Cited by 77 publications

References 134 publications

Machine Learning for the Detection and Diagnosis of Anomalies in Applications Driven by Electric Motors

Machine Learning for the Detection and Diagnosis of Anomalies in Applications Driven by Electric Motors

Estimation of Excitation Current of a Synchronous Machine Using Machine Learning Methods

Fault diagnosis in asynchronous motors based on an optimal deep bidirectional long short-term memory networks

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