HUMS2023 Data Challenge Result Submission
This is an incremental approach for domain-specific fault detection, likely in mechanical or industrial systems.
The authors tackled early detection of faults by analyzing scalogram images from Continuous Wavelet Transform and statistical features like mean and standard deviation, achieving detection through methods such as ARIMA for progression tracking.
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.