Abstract
This paper aims to develop an efficient pattern recognition method for engine fault end-to-end detection based on the echo state network (ESN) and multi-verse optimizer (MVO). Bispectrum is employed to transform the one-dimensional time-dependent vibration signal into a two-dimensional matrix with more impact features. A sparse input weight-generating algorithm is designed for the ESN. Furthermore, a deep ESN model is built by fusing fixed convolution kernels and an autoencoder (AE). A novel traveling distance rate (TDR) and collapse mechanism are studied to optimize the local search of the MVO and speed it up. The improved MVO is employed to optimize the hyper-parameters of the deep ESN for the two-dimensional matrix recognition. The experiment result shows that the proposed method can obtain a recognition rate of 93.10% in complex engine faults. Compared with traditional deep belief networks (DBNs), convolutional neural networks (CNNs), the long short-term memory (LSTM) network, and the gated recurrent unit (GRU), this novel method displays superior performance and could benefit the fault end-to-end detection of rotating machinery.
Highlights
As regular power machinery, the diesel engine has superior output torque and fuel economy, which secures its irreplaceable role in the industry, agriculture, and so on
The original echo state network (ESN), the deep belief networks (DBNs), the long short-term memory (LSTM), the gated recurrent unit (GRU), and the convolutional neural networks (CNNs) are analyzed for comparisons
The results show that the DBN and the original ESN cannot mine deep information from limited one-dimensional signals
Summary
The diesel engine has superior output torque and fuel economy, which secures its irreplaceable role in the industry, agriculture, and so on. Under worsening energy and environmental crises, many countries are creating stringent legislation for diesel engines [1]. This presents a challenge for researchers; on the other hand, many technologies are developing rapidly as a result of this opportunity [2]. Engine fault detection has developed from breakdown maintenance to regular maintenance and is gradually developing into predictive maintenance [4]. Vibration analysis does not invade the engine block and can detect multiple kinds of faults, so it is currently considered to be one of the strongest potential methods [8]. The engine has many excitation sources; sensors are usually placed on the block and cylinder head cover to collect vibration signals synthetically. Appropriate algorithms should be researched to recognize faults accurately [9]
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