Intelligent Bearing Fault Diagnosis Method Combining Mixed Input and Hybrid CNN-MLP model

TL;DR

This paper introduces a hybrid CNN-MLP model that uses mixed input data from shaft-mounted sensors for accurate and localized rolling bearing fault diagnosis, outperforming individual models.

Contribution

The paper presents a novel hybrid CNN-MLP diagnostic approach combining mixed input data for improved bearing fault detection and localization.

Findings

High detection accuracy of 99.6% for bearing faults

Hybrid model outperforms standalone CNN and MLP models

Effective fault localization using shaft-mounted sensor data

Abstract

Rolling bearings are one of the most widely used bearings in industrial machines. Deterioration in the condition of rolling bearings can result in the total failure of rotating machinery. AI-based methods are widely applied in the diagnosis of rolling bearings. Hybrid NN-based methods have been shown to achieve the best diagnosis results. Typically, raw data is generated from accelerometers mounted on the machine housing. However, the diagnostic utility of each signal is highly dependent on the location of the corresponding accelerometer. This paper proposes a novel hybrid CNN-MLP model-based diagnostic method which combines mixed input to perform rolling bearing diagnostics. The method successfully detects and localizes bearing defects using acceleration data from a shaft-mounted wireless acceleration sensor. The experimental results show that the hybrid model is superior to the CNN and MLP models operating separately, and can deliver a high detection accuracy of 99,6% for the bearing faults compared to 98% for CNN and 81% for MLP models.