# Adaptive lift chiller units fault diagnosis model based on machine learning

## TL;DR

This paper introduces a new machine learning model for early fault detection in chiller units, improving accuracy and stability over traditional methods.

## Contribution

A novel hybrid model combining HINGO, LSSVM, and IAdaBoost for enhanced early fault diagnosis in chillers.

## Key findings

- HINGO improves the algorithm's search ability through strategies like Lévy flight and nonlinear decreasing factors.
- The HINGO-LSSVM-IAdaBoost model outperforms traditional methods in early fault diagnosis of chiller units.
- The model was validated using ASHRAE RP-1043 air conditioning fault samples.

## Abstract

The early minor faults generated by the chiller in operation are not easy to perceive, and the severity will gradually increase with time. The traditional fault diagnosis method has low accuracy and poor stability for early fault diagnosis. In this paper, a fault diagnosis model of Chiller is designed by combining least squares support vector machine (LSSVM) optimized by hybrid improved northern goshawk optimization algorithm (HINGO) and improved IAdaBoost ensemble learning algorithm. HINGO enhances the uniformity of the initial population distribution by means of refraction opposition-based learning strategy in initialization, and improves the local and global search ability of the algorithm by means of sine and cosine strategy, Lévy flight and nonlinear decreasing factor in the search stage. The HINGO-LSSVM-IAdaBoost model is trained and validated on the typical air conditioning fault samples of ASHRAE RP-1043. Compared with the traditional methods, the HINGO-LSSVM-IAdaBoost model shows obvious advantages for the early fault diagnosis of chiller units.