Multimodal Bearing Fault Classification Under Variable Conditions: A 1D CNN with Transfer Learning

TL;DR

This paper introduces a multimodal 1D CNN approach with transfer learning for bearing fault classification, achieving high accuracy and robustness across variable conditions, enhancing reliability in industrial machinery monitoring.

Contribution

It proposes a novel multimodal 1D CNN framework with transfer learning strategies that improve fault detection accuracy and adaptability under different operating conditions.

Findings

Achieved 96% accuracy under baseline conditions with regularization.

Transfer learning strategies enhance robustness across different operating scenarios.

Trade-offs between computational complexity and accuracy are discussed.

Abstract

Bearings play an integral role in ensuring the reliability and efficiency of rotating machinery - reducing friction and handling critical loads. Bearing failures that constitute up to 90% of mechanical faults highlight the imperative need for reliable condition monitoring and fault detection. This study proposes a multimodal bearing fault classification approach that relies on vibration and motor phase current signals within a one-dimensional convolutional neural network (1D CNN) framework. The method fuses features from multiple signals to enhance the accuracy of fault detection. Under the baseline condition (1,500 rpm, 0.7 Nm load torque, and 1,000 N radial force), the model reaches an accuracy of 96% with addition of L2 regularization. This represents a notable improvement of 2% compared to the non-regularized model. In addition, the model demonstrates robust performance across three distinct operating conditions by employing transfer learning (TL) strategies. Among the tested TL variants, the approach that preserves parameters up to the first max-pool layer and then adjusts subsequent layers achieves the highest performance. While this approach attains excellent accuracy across varied conditions, it requires more computational time due to its greater number of trainable parameters. To address resource constraints, less computationally intensive models offer feasible trade-offs, albeit at a slight accuracy cost. Overall, this multimodal 1D CNN framework with late fusion and TL strategies lays a foundation for more accurate, adaptable, and efficient bearing fault classification in industrial environments with variable operating conditions.