Multi-fault diagnosis with wavelet assisted stacked image fusion and dual branch CNN

The rotating machine components are interconnected. If the machines are not monitored properly, it causes damage to the connected parts, causing catastrophic failure. Dependability on a single sensor or sensors of the same modality for multi-fault diagnosis influences decision-making. Therefore, multi-modality multi-sensor fusion has been used to gather distinct information. This work proposes a Wavelet Assisted Stacked Image Fusion (WASIF) with Dual Branch Convolutional Neural Network (DBCNN) to effectively diagnose multi-faults. At first, various multi-fault conditions in a test rig are introduced, which consist of conditions like faulty motor, faulty bearing, mechanical unbalance, shaft misalignment and their combinations. Thereafter, vibration and acoustic data are acquired at a varying speed condition. The acquired signatures are pre-processed and converted into time-frequency spectrums using Fourier Synchrosqueezed Transform (FSST). The vibration and acoustic spectrums are fused into vibro-acoustic spectrums using the WASIF technique. The generated spectrums are used for DBCNN training for multi-fault classification, and 98.8 % overall classification accuracy is achieved. In this paper, a separate ablation experiment is done along with a published literature comparison to justify the effectiveness of the selected parameters. The proposed fusion-based multi-fault diagnosis strategy would be helpful to the industries for incipient fault detection, inventory management and workforce allocation.