# Multi-Source Diagnosis of Bearing Faults Using Interpretable Boosted Trees

## TL;DR

This paper uses explainable AI to diagnose bearing faults in motors by analyzing vibration, sound, and current data.

## Contribution

The novel use of XAI and SHAP values to interpret and compare multisensor data fusion for fault diagnosis in induction motors.

## Key findings

- Boosting techniques provide high diagnostic accuracy for bearing faults.
- SHAP values reveal which sensor data and domains most contribute to accurate diagnosis.
- Multisensor data fusion improves fault diagnosis performance.

## Abstract

The early detection and diagnosis of faults in induction motors is vital in today’s industry, since these are the motors used for the largest number of applications in the industrial environment and failure to detect a fault early can lead to significant losses. Bearing faults are the main problems detected in induction motors and several techniques have been developed to detect them. The use of the information contained in the motor vibrations is the main traditional source for its diagnosis, although there are also proposals that use the supply current, or the sound of the motor. Furthermore, these variables can be used in the time domain or in the frequency domain. The purpose of this work is to use explainable artificial intelligence (XAI) to determine which of these variables, and in which domain, contributes most to a correct diagnosis and how much can be gained in diagnosis by using multisensor data fusion. To carry out this comparison in the most objective way possible, a model selection procedure is proposed and boosting techniques are considered that prove to give a very precise diagnosis. The obtained diagnostic rules are then interpreted using SHAP values, a recent interpretation technique for complex classification procedures.