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Purpose <p>This work introduces a black box intelligent fault classification algorithm designed to distinguish between classes that result from minimal changes caused by varying numbers of faults present in the twin rotor multi-input–multi-output system (TRMS).</p> Methods <p>Initially, faults present in steady state vibration signals are classified using features that are extracted from classical signal processing tools. Subsequently, results are improved by combining features extracted through the proposed algorithm. The methodology employs a long short term memory (LSTM) network to extract features for stationary signals, and it combines with convolutional neural network (CNN) to extract features from the transform of both stationary and nonstationary signals.</p> Results <p>The proposed fault detection algorithm has been tested using vibration signals collected from steady and unsteady state TRMS encompassing healthy and faulty scenarios. Maximum accuracy of 0.96 and 0.77 have been achieved in the case of steady state and unsteady state, respectively.</p> Conclusion <p>The study offers novel feature extraction technique using LSTM for steady state, and LSTM with CNN for unsteady state from classical signal processed data to improve the accuracy of fault classification.</p>

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Fault Detection of Twin Rotor MIMO System with Varying Severities Using LSTM and CNN Autoencoder

  • 期刊论文

  • 发表日期：

    2025年2月5日

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  • Debashish Nanda,
  • Sanjoy K. Ghoshal

摘要

Purpose

This work introduces a black box intelligent fault classification algorithm designed to distinguish between classes that result from minimal changes caused by varying numbers of faults present in the twin rotor multi-input–multi-output system (TRMS).

Methods

Initially, faults present in steady state vibration signals are classified using features that are extracted from classical signal processing tools. Subsequently, results are improved by combining features extracted through the proposed algorithm. The methodology employs a long short term memory (LSTM) network to extract features for stationary signals, and it combines with convolutional neural network (CNN) to extract features from the transform of both stationary and nonstationary signals.

Results

The proposed fault detection algorithm has been tested using vibration signals collected from steady and unsteady state TRMS encompassing healthy and faulty scenarios. Maximum accuracy of 0.96 and 0.77 have been achieved in the case of steady state and unsteady state, respectively.

Conclusion

The study offers novel feature extraction technique using LSTM for steady state, and LSTM with CNN for unsteady state from classical signal processed data to improve the accuracy of fault classification.