A hybrid multiple sensor fault detection, diagnosis and reconstruction algorithm for chiller plants

Abstract

In a chiller plant, primary or critical sensors are used to control the system operation while secondary sensors are installed to monitor the performance/health of individual equipment. Current sensor fault detection and diagnosis (SFDD) approaches are not applicable to secondary sensors which usually are not involved in the system control. Consequently, a hybrid multiple sensor fault detection, diagnosis and reconstruction (HMSFDDR) algorithm for chiller plants was developed. Machine learning and pattern recognition were used to predict the primary sensor faults through the comparison of the weekly performance curves. With the primary sensor signals reconstructed, the secondary sensor faults were estimated based on mass and energy balance. By applying the algorithm with various logged plant data and comparison with site checking results, a maximum of 75% effectiveness could be achieved. The merits of the present approach were further justified through off-site sensor testing which reinforced the usefulness of proposed HMSFDDR algorithm.

KEYWORDS:

• Fault detection and diagnosis
• big data analytics
• machine learning
• pattern recognition
• chiller plant
• sensor faults

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The participants of this study did not give written consent for their data to be shared publicly, so due to the sensitive nature of the research supporting data is not available.

Additional information

Funding

The work described in this article was financially supported by Siemens Limited through the Contract Research (Project No. 9231309).