HUMS2023 Data Challenge Result Submission

TL;DR

This paper presents a straightforward approach for early fault detection using signal analysis techniques like scalogram visualization, statistical feature extraction, and ARIMA modeling to monitor signal progression.

Contribution

The study introduces a simple, multi-faceted method combining wavelet-based visualization, statistical analysis, and ARIMA for early fault detection in signals.

Findings

Scalogram analysis helps visualize signal features.

Statistical features assist in fault detection.

ARIMA models track signal progression over time.

Abstract

We implemented a simple method for early detection in this research. The implemented methods are plotting the given mat files and analyzing scalogram images generated by performing Continuous Wavelet Transform (CWT) on the samples. Also, finding the mean, standard deviation (STD), and peak-to-peak (P2P) values from each signal also helped detect faulty signs. We have implemented the autoregressive integrated moving average (ARIMA) method to track the progression.