An intelligent system to monitor refrigeration devices. Issue 5 (13th July 2017)

• Record Type: Journal Article
• Authors: Kužnar, Damjan; Piltaver, Rok; Gradišek, Anton; Gams, Matjaž; Luštrek, Mitja
• Other Names: Neagu Daniel guestEditor.
• Is Part Of: Expert systems. Volume 34:Issue 5(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-07-13
• Subjects: event classification -- event detection -- intelligent systems -- machine learning -- refrigeration devices -- time series; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12199
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Refrigeration systems have been a vital component of our lives for more than a century. Apart from storing food, they are used to store sensitive goods such as pharmaceutical products or reactive chemicals. The deterioration of the refrigeration system performance due to aging or malfunction directly affects the quality of stored goods. Therefore, an early detection of deviation in performance is an important task. This paper presents a system that monitors operation of refrigeration devices and alerts the user to possible irregularities in the operation run. The emphasis is on recognition of gradual changes of performance that indicate upcoming hardware problems. The system consists of 2 modules: human‐defined expert rules and machine learning. The machine‐learning module learns to recognize abnormal behaviour of devices automatically. Furthermore, it can distinguish between different abnormal events and allow the user to classify some of the types as normal, so that they not longer raise an alarm. The machine learning was evaluated by comparing its recognition of abnormal events and classification accuracy of such events to the performance of a human operator. The system can in principle be adapted to any electronic device that periodically applies some system for sustaining a predefined quality (e.g., temperature).