Planetary gearbox fault diagnosis using bidirectional-convolutional LSTM networks

Application of recurrent neural network to mechanical fault diagnosis: a review

A Review of Data-Driven Machinery Fault Diagnosis Using Machine Learning Algorithms

The harmonic reducer is a key component of the industrial robot. Its reliability has significant influence on the consecutive operation of the industrial robot. However, its failure rate is high due to the complicated internal structure and long-term working conditions of large load and high torque. To identify the fault type of the harmonic reducer under different working conditions, this article proposes the joint wavelet regional correlation threshold denoising (WRCTD) algorithm and convolutional neural network-long short term memory (CNN-LSTM) fault detection method. Firstly, the WRCTD algorithm utilizes the regional correlation of the wavelet decomposition coefficients and the $3\sigma $ criterion to suppress noise in the raw sensor data. Then, the CNN-LSTM model could mine hidden features of the processed sensor data to identify fault correctly. To evaluate the proposed method, the vibration signals of the harmonic reducer under different working conditions are collected by establishing a test rig. Comparative experimental results show that the proposed WRCTD algorithm can significantly improve the accuracy of the fault detection method. The specific values for the CNN-LSTM method and the LSTM method are 9.5% and 12.2%, respectively. In addition, the proposed CNN-LSTM method has better performance than traditional methods. It achieves the fault detection over 94.0%, which is 1.5%, 2.0%, 2.0%, 1.9%, and, 1.9% higher than the highest accuracy values of CNN, LSTM, and SVM methods in five experiments.

Fault Detection of the Harmonic Reducer Based on CNN-LSTM With a Novel Denoising Algorithm

Abstract The integration of renewable energy sources (RESs) has become more attractive to provide electricity to rural and remote areas, which increases the reliability and sustainability of the electrical system, particularly for areas where electricity extension is difficult. Despite this, the integration of hybrid RESs is accompanied by many problems as a result of the intermittent and unstable nature of RESs. The extant literature has discussed the integration of RESs, but it is not comprehensive enough to clarify all the factors that affect the integration of RESs. In this paper, a comprehensive review is made of the integration of RESs. This review includes various combinations of integrated systems, integration schemes, integration requirements, microgrid communication challenges, as well as artificial intelligence used in the integration. In addition, the review comprehensively presents the potential challenges arising from integrating renewable resources with the grid and the control strategies used. The classifications developed in this review facilitate the integration improvement process. This paper also discusses the various optimization techniques used to reduce the total cost of integrated energy sources. In addition, it examines the use of up-to-date methods to improve the performance of the electrical grid. A case study is conducted to analyze the impact of using artificial intelligence when integrating RESs. The results of the case study prove that the use of artificial intelligence helps to improve the accuracy of operation to provide effective and accurate prediction control of the integrated system. Various optimization techniques are combined with ANN to select the best hybrid model. PSO has the fast convergence rate for reaching to the minimum errors as the Normalized Mean Square Error (NMSE) percentage reaches 1.10% in 3367.50 s. 

/pdf/artificial-intelligence-applications-for-microgrids-cbsfo9bm.pdf

Artificial intelligence applications for microgrids integration and management of hybrid renewable energy sources

The gearbox is one of the most fragile parts of a wind turbine (WT). Fault diagnosis of the WT gearbox is of great importance to reduce operation and maintenance (O&M) costs and improve cost-effectiveness. At present, intelligent fault diagnosis methods based on long short-term memory (LSTM) networks have been widely adopted. As the traditional softmax loss of an LSTM network usually lacks the power of discrimination, this paper proposes a fault diagnosis method for wind turbine gearboxes based on optimized LSTM neural networks with cosine loss (Cos-LSTM). The loss can be converted from Euclid space to angular space by cosine loss, thus eliminating the effect of signal strength and improve the diagnosis accuracy. The energy sequence features and the wavelet energy entropy of the vibration signals are used to evaluate the Cos-LSTM networks. The effectiveness of the proposed method is verified with the fault vibration data collected on a gearbox fault diagnosis experimental platform. In addition, the Cos-LSTM method is also compared with other classic fault diagnosis techniques. The results demonstrate that the Cos-LSTM has better performance for gearbox fault diagnosis.

Fault Diagnosis of Wind Turbine Gearbox Based on the Optimized LSTM Neural Network with Cosine Loss.

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Fault Diagnosis of Wind Turbine Gearbox Based on the Optimized LSTM Neural Network with Cosine Loss.