# Semi-supervised Approach to Soft Sensor Modeling for Fault Detection in Industrial Systems with Multiple Operation Modes

## TL;DR

This paper presents a semi-supervised soft sensor modeling method that leverages incomplete data and transition information to detect faults in industrial systems with multiple operation modes.

## Contribution

It introduces a novel semi-supervised approach that incorporates transition properties to improve soft sensor accuracy in multi-mode systems.

## Key findings

- Effective fault detection in air-conditioning systems
- Improved soft sensor performance with incomplete datasets
- Potential applicability to various industrial processes

## Abstract

In industrial systems, certain process variables that need to be monitored for detecting faults are often difficult or impossible to measure. Soft sensor techniques are widely used to estimate such difficult-to-measure process variables from easy-to-measure ones. Soft sensor modeling requires training datasets including the information of various states such as operation modes, but the fault dataset with the target variable is insufficient as the training dataset. This paper describes a semi-supervised approach to soft sensor modeling to incorporate an incomplete dataset without the target variable in the training dataset. To incorporate the incomplete dataset, we consider the properties of processes at transition points between operation modes in the system. The regression coefficients of the operation modes are estimated under constraint conditions obtained from the information on the mode transitions. In a case study, this constrained soft sensor modeling was used to predict refrigerant leaks in air-conditioning systems with heating and cooling operation modes. The results show that this modeling method is promising for soft sensors in a system with multiple operation modes.