Intelligent application of fault detection and isolation on HVAC system

University of Technology, Sydney. Faculty of Engineering and Information Technology.Efficient heating, ventilation, and air-conditioning (HVAC) systems are one of the big challenges today around the world. The fault detection and isolation (FDI) play a significant role in the monitoring, repairing and maintaining of technical systems for the final destination of safety and cost reduction. FDI makes an infrastructure to effectively reduce total cost of maintenance and thus increases the capacity utilization rates of equipment. Reduction of energy wasting in the system by real-time fault detection is another goal. Among all HVAC system’s studies, the focus of this thesis is on developing of fast and reliable FDI structure that can cover all subsections of HVAC system including cooling tower, chiller and air handling units (AHU) which greatly affect building energy consumption and indoor environment quality. The first stage of this study is to develop and validates a mathematical HVAC model then follows by simulation and sensitivity analysis. The simulation makes a good capability of producing artificial fault free and faulty data for review of any upcoming failure over the HVAC system. These data with wide range of fault severities can be used to assess the performance of HVAC automated fault detection and isolation (AFDI) system. Two categories of process history diagnosis methods have been reviewed and assessed for the development of AFDI algorithms at second stage of this study. Principal component analysis (PCA) and support vector machine (SVM) classification are two chosen algorithm which have been analysed in depth and initially tested by simulated data from stage one. This review has been continued by developing online SVM algorithm with incremental learning technique and then tested both on simulated and operational data. An experimental rig is designed and applied in the last stage of this research. This setup is configured inside the HVAC laboratory of UTS to collect operational data for the operating test. Operational data as outcome of this stage was then used for test of developed AFDI from last stage. Artificial neural network (ANN) algorithm compressed in frame of black box model for fault free reference. Finally, a combination of black box model and developed AFDI was tested and evaluated for cooling tower and air handling unit (AHU) faults based on operational data. The result shows increasing of robustness, performance and accuracy for the proposed AFDI over the operational data

Online support vector machine application for model based fault detection and isolation of HVAC system

Abstract—Preventive maintenance plays an important role in Heating, Ventilation and Air Conditioning (HVAC) system. One cost effective strategy is the development of analytic fault detection and isolation (FDI) module by online monitoring the key variables of HAVC systems. This paper investigates realtime FDI for HAVC system by using online Support Vector Machine (SVM), by which we are able to train a FDI system with manageable complexity under real time working conditions. It is also proposed a new approach which allows us to detect unknown faults and updating the classifier by using these previously unknown faults. Based on the proposed approach, a semi unsupervised fault detection methodology has been developed for HVAC system

Exploiting macrophage autophagy-lysosomal biogenesis as a therapy for atherosclerosis

Macrophages specialize in removing lipids and debris present in the atherosclerotic plaque. However, plaque progression renders macrophages unable to degrade exogenous atherogenic material and endogenous cargo including dysfunctional proteins and organelles. Here we show that a decline in the autophagy-lysosome system contributes to this as evidenced by a derangement in key autophagy markers in both mouse and human atherosclerotic plaques. By augmenting macrophage TFEB, the master transcriptional regulator of autophagy-lysosomal biogenesis, we can reverse the autophagy dysfunction of plaques, enhance aggrephagy of p62-enriched protein aggregates and blunt macrophage apoptosis and pro-inflammatory IL-1β levels, leading to reduced atherosclerosis. In order to harness this degradative response therapeutically, we also describe a natural sugar called trehalose as an inducer of macrophage autophagy-lysosomal biogenesis and show trehalose's ability to recapitulate the atheroprotective properties of macrophage TFEB overexpression. Our data support this practical method of enhancing the degradative capacity of macrophages as a therapy for atherosclerotic vascular disease

Design of a Micro-Probe For Direct Measurement of Convection Heat Transfer on a Vertical

A proximity probe with two k-type thermocouples, 1.5 mm apart, was designed, built to simultaneously measure local surface and air temperatures on the PV and to quantify local convention heat transfer coefficient. Experimental investigations of natural convection on a vertical photovoltaic (PV) panel exposed to solar radiations are presented. The variation of non-isothermal surface temperature of a PV is expressed with a second-order polynomial relation. In the absence of any correlation to predict the natural convection heat transfer coefficient on a PV, experimental results are presented in the form of variations of the local Nusselt numbers (Nuz), and the average Nusselt numbers (Nu), with Rayleigh number (Ra). The variations are best expressed with a power law correlation form of Nu=a*(Ra)^b for the range 10^6 <Ra<10^8 where a and b are determined experimentally. The power-law correlations for photovoltaic were compared with a number of correlations developed from natural convection research in laboratories. The analysis showed that for a given Rayleigh number, the predicted value of Nusselt number by the PV correlations are within the range covered by others. However, the PV correlations overestimate the Nusselt number by 20% in Rayleigh number higher than 10^6 . The work is in progress to further extend the correlation to predict the combined radiation and convection on all PV configurations, as required in the efficient design of building integrated photovoltaic (BIPV) systems

Comprehensive analysis for air supply fan faults based on HVAC mathematical model

Due to the growing demand on high efficient heat ventilation and air conditioning (HVAC) systems, how to improve the efficiency of HVAC system regarding reduces energy consumption of system has become one of the critical issues. Reports indicate that efficiency and availability are heavily dependent upon high reliability and maintainability. Recently, the concept of e-maintenance has been introduced to reduce the cost of maintenance. In e-maintenance systems, the fault detection and isolation (FDI) system plays a crucial role for identifying failures. Finding healthy HVAC source as the reference for health monitoring is the main aim in this area. To dispel this concern a comprehensive transient model of heat ventilation and air conditioning (HVAC) systems is developed in this study. The transient model equations can be solved efficiently using MATLAB coding and simulation technique. Our proposed model is validated against real HVAC system regarding different parts of HVAC. The developed model in this study can be used for a pre tuning of control system and put to good use for fault detection and isolation in order to accomplish high-quality health monitoring and result in energy saving. Fan supply consider as faulty device of HVAC system with six fault type. A sensitivity analysis based on evaluated model shows us three features are sensitive to all faults type and three auxiliary features are sensitive to some faults. The magnitude and trait of features are a good potential for automatic fault tolerant system based on machine learning systems. © (2012) Trans Tech Publications

Comprehensive Analysis for Air Supply Fan Faults Based on HVAC Mathematical Model

Due to the growing demand on high efficient heat ventilation and air conditioning (HVAC) systems, how to improve the efficiency of HVAC system regarding reduces energy consumption of system has become one of the critical issues. Reports indicate that efficiency and availability are heavily dependent upon high reliability and maintainability. Recently, the concept of e-maintenance has been introduced to reduce the cost of maintenance. In e-maintenance systems, the fault detection and isolation (FDI) system plays a crucial role for identifying failures. Finding healthy HVAC source as the reference for health monitoring is the main aim in this area. To dispel this concern a comprehensive transient model of heat ventilation and air conditioning (HVAC) systems is developed in this study. The transient model equations can be solved efficiently using MATLAB coding and simulation technique. Our proposed model is validated against real HVAC system regarding different parts of HVAC. The developed model in this study can be used for a pre tuning of control system and put to good use for fault detection and isolation in order to accomplish high-quality health monitoring and result in energy saving. Fan supply consider as faulty device of HVAC system with six fault type. A sensitivity analysis based on evaluated model shows us three features are sensitive to all faults type and three auxiliary features are sensitive to some faults. The magnitude and trait of features are a good potential for automatic fault tolerant system based on machine learning systems. © (2012) Trans Tech Publications

Robust fault tolerant application for HVAC system based on combination of online SVM and ANN black box model

Efficient heating, ventilation, and air-conditioning (HVAC) systems are one of the big challenges today around the world. The fault detection and isolation (FDI) play a significant role in the monitoring, repairing and maintaining of technical systems for the final destination of cost reduction. FDI makes it possible to reduce total cost effective of maintenance and thus increase the capacity utilization rates of equipment. Reduction of energy wasting in the system by on time fault detection is another goal. Therefore, this work proposes a new fault detector based on a black box Artificial Neural Network (ANN) model and online support vector machines (SVM) classifier which integrates a dimension reduction scheme to analyze the failure of air fan supply and dampers fault. The key advantage of this algorithm is to make robustness for SVM to recognize a faulty condition with unexpected sensors values. The ANN generates a high accurate model which is based reference for SVM classifier. Now by using this black box model we make possibility of robustness for SVM to increase detection probability. Finally, a series of faulty experimental data are applied to evaluate the effectiveness of the robust classifier. Final results show that online SVM can detect accurately the air supply fan fault and damper fault of a HVAC system with minimum usage data. It is also outperforms offline SVM on such energy systems for classification. © 2013 EUCA