A review on machinery diagnostics and prognostics implementing condition-based maintenance

This paper is the second of a two-part review of methods for automated fault detection and diagnostics (FDD) and prognostics whose intent is to increase awareness of the HVAC&R research and development community to the body of FDD and prognostics developments in other fields as well as advancements in the field of HVAC&R. The first part of the review focused on generic FDD and prognostics, provided a framework for categorizing methods, described them, and identified their primary strengths and weaknesses (Katipamula and Brambley 2005). In this paper we address research and applications specific to the fields of HVAC&R, provide a brief discussion on the current state of diagnostics in buildings, and discuss the future of automated diagnostics in buildings.

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Methods for Fault Detection, Diagnostics and Prognostics for Building Systems - A Review Part II

This paper presents a method for automated detection and diagnosis of faults in vapor compression air conditioners that only requires temperature measurements, and one humidity measurement. The differences between measured thermodynamic states and predicted states obtained from models for normal performance (residuals) are used as performance indices for both fault detection and diagnosis. For fault detection, statistical properties of the residuals for current and normal operation are used to classify the current operation as faulty or normal. A diagnosis is performed by comparing the directional change of each residual with a generic set of rules unique to each fault. This diagnostic technique does not require equipment-specific learning, is capable of detecting about a 5% loss of refrigerant, and can distinguish between refrigerant leaks, condenser fouling, evaporator fouling, liquid line restrictions, and compressor valve leakage.

A Statistical, Rule-Based Fault Detection and Diagnostic Method for Vapor Compression Air Conditioners

This paper identifies important faults and their performance impacts for rooftop air conditioners. The frequencies of occurrence and the relative costs of service for different faults were estimated through analysis of service records. Several of the important and difficult to diagnose refrigeration cycle faults were simulated in the laboratory. Also, the impacts on several performance indices were quantified through transient testing for a range of conditions and fault levels. The transient test results indicated that fault detection and diagnostics could be performed using methods that incorporate steady-state assumptions and models. Furthermore, the fault testing led to a set of generic rules for the impacts of faults on measurements that could be used for fault diagnoses. The average impacts of the faults on cooling capacity and coefficient of performance (COP) were also evaluated. Based upon the results, all of the faults are significant at the levels introduced, and should be detected and diagnosed ...

Common faults and their impacts for rooftop air conditioners

This paper presents a general methodology for developing a steady-state detector for a vapor compression system based on a moving window and using standard deviations of seven measurements selected as features. The feature thresholds and optimized moving window size were based upon steady-state no-fault tests and startup transient tests. The study showed that evaporator superheat and condenser subcooling were sufficient for determining the onset of steady-state during the startup transient. However, they misidentified steady-state during indoor temperature change tests where evaporator saturation temperature and air temperature change across the evaporator were needed for proper steady-state identification. Hence, the paper recommends including all fault detection and diagnosis (FDD) features in the steady-state detector to ensure the robustness of the detector because different features may play key roles with different transients.

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Design of a steady-state detector for fault detection and diagnosis of a residential air conditioner

A prototype condition monitoring and diagnostic system has been developed for compression refrigeration plants, which can be used under variable operational conditions. Based on a combination of causal analysis, expert knowledge and simulated failure modes, a failure mode symptom matrix has been created. Healthy system behaviour is predicted based on a regression analysis model. Using multi-valued (or ‘fuzzy’) logic, real-time recognition of failure modes, at an early stage, proved to be possible. Future developments for improvement of diagnostic systems in compression refrigeration plants are discussed.

J. Klein Woud

Papers

On-line failure diagnosis for compression refrigeration plants