Data mining based sensor fault diagnosis and validation for building air conditioning system

Hou, Zhenshan; Lian, Zhiwei; Yao, Ye; Yuan, Xinjian

doi:10.1016/j.enconman.2005.11.010

Cited by 86 publications

(36 citation statements)

References 23 publications

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“…In fault diagnosis there are few publications including the work of Hou et al (2006) on yield enhancement in semiconductor manufacturing and Zhang et al (2005) who used data mining to extract features in fault diagnosis of machines.…”

Section: Data Mining In Quality Maintenance and Fault Diagnosismentioning

confidence: 99%

“…develop a hybrid learning-based model for on-line intelligent monitoring and diagnosis of manufacturing processes using a knowledge base, NNs and GAs. Hou et al(2006) introduce a strategy based on data mining for detection and diagnosis of faults in heating, ventilating and air conditioning systems using a combined rough set approach and NN.…”

Section: Hybrid Ai In Quality Maintenance and Fault Diagnosismentioning

confidence: 99%

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A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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Purpose: The use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research.Design/methodology/approach: The paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004(Kobbacy et al. 2007. Like the previous survey, it uses the Elsevier's ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings:The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research.

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Section: Data Mining In Quality Maintenance and Fault Diagnosismentioning

confidence: 99%

Section: Hybrid Ai In Quality Maintenance and Fault Diagnosismentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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“…Salsbury and Diamond [12] used simulation to predict performance targets and compare monitored system outputs for performance validation and energy analysis. Hou et al [13] combined a rough set approach with a neural network algorithm to build a model based on past HVAC performance data. The model was intended to detect and diagnose sensor faults in HVAC systems.…”

Section: Introductionmentioning

confidence: 99%

Virtual models of indoor-air-quality sensors

Kusiak

Zheng

2010

Applied Energy

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“…This method is validated to evaluate soft sensor faults (biases) for temperature sensors and flow meters in central chilling plant. Other mathematical models including blackbox multivariate polynomial methods, specifically radial basis function and multilayer perceptron, the generic physical component model [11,12], artificial neural network [14], rough set approach [15], transient pattern analysis [16] and others, are used to get deviations for well suited automated FDD in HVAC equipments and systems.…”

Section: Introductionmentioning

confidence: 99%