Multi-sensor fusion-based solution for fault diagnosis of a gearbox using DataCivet

Background and Objectives

In a mechanical transmission system, gearbox fault diagnosis is a critical instrument for ensuring safe operation. Such mechanical equipment acoustic and vibratory observations may be sensitive to flaws. The interference transmitted across different routes, as well as the vibrating signals emitted by a single sensor, may not be enough to assess the health of a gearbox. Multi-sensor data fusion is being attempted for gearbox malfunction diagnosis in Kaveri engine gearbox to address these concerns.

The use of vibration signals for fault detection and diagnosis is the foundation for monitoring the health of gearboxes in the engine. Non-stationary load, friction, nonlinear stiffness, and severe noise all affect vibration signals. The most important aspects of analysing a gearbox failure in the presence of noise are to efficiently extract fault feature information, minimise uncertainty in the identification process, and precisely determine the fault state. Furthermore, the vibration data obtained by a single sensor in the engine could be unstable or untrustworthy. Acoustic signals are commonly employed to monitor health conditions since they are sensitive to vibrating bodies rather than vibrating sensors, allowing them to detect folds at an early stage. Sensor fusion can combine the benefits of auditory and vibratory signals, resulting in a signal with less uncertainty than the signals derived from the individual sensors. The challenges of signal fusion include (1) feature extraction from various types of sensory data (2) level of fusion selection In order to accomplish so, you'll need a lot of subject knowledge.


Fusion of multiple signals and the classification of faults to monitor the health condition of the gearbox. In general, the task of assessing the health of gearbox and prognostics consists of 4 steps:

Data acquisition.

Signal preprocessing and feature extraction.

Feature selection and data fusion.


To remove interference and highlight fault information, energy operator and time synchronous averaging are performed to the multiple sensor data. The statistical features from the raw and pre-processed signals are integrated to form the final feature set. As a first step, feature extraction is applied. Secondly, the feature selection algorithm combines min-redundancy, max- relevance and distance evaluation technique to provide the optimal feature set. Thirdly, a bidirectional LSTM architecture will be applied to determine the fault conditions of the gearbox and classify the different types of faults.


Investigate different gearbox failure modes.

Collect the data from multiple sensors and fuse the data.

Data exploration and analysis.

Experimenting different feature selection methods.

Initial study of appropriate machine learning models.