Heating, Ventilation and Air Conditioning (HVAC) systems constitute the largest portion of energy consumption equipment in residential and commercial facilities. Real-time health monitoring and fault diagnosis is essential for reliable and uninterrupted operation of these systems. Existing fault detection and diagnosis (FDD) schemes for HVAC systems are only suitable for a single operating mode with small numbers of faults, and most of the schemes are systemspecific. A generic real-time FDD scheme, applicable to all possible operating conditions, can significantly reduce HVAC equipment downtime, thus improving the efficiency of building energy management systems. This paper presents a FDD methodology for faults in centrifugal chillers. The FDD scheme compares the diagnostic performance of three data-driven techniques, namely support vector machines (SVM), principal component analysis (PCA), and partial least squares (PLS). In addition, a nominal model of a chiller that can predict system response under new operating conditions is developed using PLS. We used the benchmark data on a 90-ton real centrifugal chiller test equipment, provided by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), to demonstrate and validate our proposed diagnostic procedure. The database consists of data from sixty four monitored variables under nominal and eight fault conditions of different severities at twenty seven operating modes.
Modern industrial systems assume different configurations to accomplish multiple objectives during different phases of operation, and the component parameters may also vary from one phase to the next. Consequently, reliability evaluation of complex multi-phased systems is a vital and challenging issue. Maximization of mission reliability of a multi-phase system via optimal asset selection is another key demand; incorporation of optimization issues adds to the complexities of reliability evaluation processes. Introduction of components having self-diagnostics and self-recovery capabilities, along with increased complexity and phase-dependent configuration variations in network architectures, requires new approaches for reliability evaluation.
This paper considers the problem of evaluating the reliability of a complex multi-phased system with self-recovery/fault-protection options. The reliability analysis is based on a colored digraph (i.e., multi-functional) model that subsumes fault trees and digraphs as special cases. These models enable system designers to decide on system architecture modifications and to determine the optimum levels of redundancy. A sum of disjoint products (SDP) approach is employed to compute system reliability. We also formulated the problem of optimal asset selection in a multi-phase system as one of maximizing the probability of mission success under random load profiles on components. Different methods (e.g., ordinal optimization, robust design, and nonparametric statistical testing) are explored to solve the problem. The resulting analytical expressions and the software tool are demonstrated on a generic programmable software-controlled switchgear, a data bus controller system and a multi-phase mission involving helicopters.
The recent advances in sensor technology, remote communication and computational capabilities, and standardized hardware/software interfaces are creating a dramatic shift in the way the health of vehicles is monitored and managed. These advances facilitate remote monitoring, diagnosis and condition-based maintenance of automotive systems. With the increased sophistication of electronic control systems in vehicles, there is a concomitant increased difficulty in the identification of the malfunction phenomena. Consequently, the current rule-based diagnostic systems are difficult to develop, validate and maintain. New intelligent model-based diagnostic methodologies that exploit the advances in sensor, telecommunications, computing and software technologies are needed.
In this paper, we will investigate hybrid model-based techniques that seamlessly employ quantitative (analytical) models and graph-based dependency models for intelligent diagnosis. Automotive engineers have found quantitative simulation (e.g. MATLAB/SIMULINK) to be a vital tool in the development of advanced control systems. The hybrid method exploits this capability to improve the diagnostic system's accuracy and consistency, utilizes existing validated knowledge on rule-based methods, enables remote diagnosis, and responds to the challenges of increased system complexity. The solution is generic and has the potential for application in a wide range of systems.
Traditional static maintenance scheduling based on lifetime data and replacement upon failure is adequate for typical power users. However, in the case of high reliability/availability-oriented industries (e.g., power systems for internet data centers have a desired availability of 0.99999 and, for semiconductor fabrication plants, have availability requirement of 0.9999999), this type of preventive maintenance scheduling is inadequate. A suitable approach in these situations is the adoption of condition-based predictive maintenance. Here the system condition is evaluated by processing the information gathered from the monitors placed at different points in the system, and maintenance is performed only when the failure/malfunction prognosis dictates. In the past, for power systems, voltages, currents, power, temperature and electromagnetic quantities had been monitored along with surface inspection and material quality tests at regular intervals. Diagnostic methods are already in place to indicate problems in industrial power systems by examining these monitored quantities. However, they lack the capability of looking into distant future. With the introduction of modern digital electronics-based smart monitors, the capability of logging power quality data at micro-second intervals, advanced signal processing tools for extracting features from collected data, and data mining techniques, a new horizon in maintenance scheduling has been unveiled. Trending techniques and techniques based on neural networks, when applied to the extracted features, enable us to predict the possible failures of individual equipment and subsystems well before they manifest. This paper considers the problem of evaluating the health indices of components of a power system by making use of the monitored power-quality data and classification techniques. Health index analysis distinguishes the healthy and risky components of the system. Results of these evaluations can be fed as inputs into a system-reliability/availability analysis tool. The reliability analysis enables analysts to decide on prioritization of the maintenance options subject to budget constraints.
The deregulation of energy markets, the ongoing advances in communication networks, the proliferation of intelligent metering and protective power devices, and the standardization of software/hardware interfaces are creating a dramatic shift in the way facilities acquire and utilize information about their power usage. The currently available power management systems gather a vast amount of information in the form of power usage, voltages, currents, and their time-dependent waveforms from a variety of devices (for example, circuit breakers, transformers, energy and power quality meters, protective relays, programmable logic controllers, motor control centers). What is lacking is an information processing and decision support infrastructure to harness this voluminous information into usable operational and management knowledge to handle the health of their equipment and power quality, minimize downtime and outages, and to optimize operations to improve productivity. This paper considers the problem of evaluating the capacity and reliability analyses of power systems with very high availability requirements (e.g., systems providing energy to data centers and communication networks with desired availability of up to 0.9999999). The real-time capacity and margin analysis helps operators to plan for additional loads and to schedule repair/replacement activities. The reliability analysis, based on computationally efficient sum of disjoint products, enables analysts to decide the optimum levels of redundancy, aids operators in prioritizing the maintenance options for a given budget and monitoring the system for capacity margin. The resulting analytical and software tool is demonstrated on a sample data center.