Input-Output Hidden Markov Model for System Health Diagnosis Under Missing Data

Sensor data can be used to diagnose the system's health. A challenge comes when the data contain missing or invalid data. It is common that sensors misread for various reasons. So, data contain missing measurements and sensor saturation. The main contribution in this paper is to implement a method based on the Input-Output Hidden Markov Model that trains the model using the missing measurements and sensor saturation, then diagnoses the system health at given operating conditions. Usually, if a data set contains some sequences with missing elements then they can be excluded from the analysis. It cleans the data set but reduces its size. This strategy knows as list-wise or case-wise deletion is less suitable for real application cases. The proposed method includes the sequences with missing data into the analysis by generating the missing elements to complete the sequence. The maximum likelihood is applied to estimate IOHMM parameters that offer substantial improvements over list-wise deletion. A numerical application with simulated data sets illustrates the method.