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

arXiv: https://arxiv.org/abs/2112.08673 · HTML: https://mlsanity.com/paper/2112.08673/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 40 references that are in the map.
- First posted on arXiv 16 Dec 2021.

## Built on by (most cited first, 4 of 4)

- [Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial](https://mlsanity.com/paper/2305.04933/) (2023)
- [SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-hoc Interpretable Fault Diagnosis with Limited Fault Samples](https://mlsanity.com/paper/2302.01599/) (2023)
- [UniFault: A Fault Diagnosis Foundation Model from Bearing Data](https://mlsanity.com/paper/2504.01373/) (2025)
- [S2S-FDD: Bridging Industrial Time Series and Natural Language for Explainable Zero-shot Fault Diagnosis](https://mlsanity.com/paper/2603.08048/) (2026)

## Abstract (arXiv)

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.