Rule-based versus probabilistic approaches to the diagnosis of faults in wastewater treatment processes

Chong, H. Gin; Walley, William J.

doi:10.1016/0954-1810(96)00003-9

Cited by 24 publications

(11 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, enough evidence is defined as that in which there are several rules supporting the hypothesis, and their combined confidence factor is larger than a specified threshold value (this confidence factor union method is selected since it is widely employed [15]). The formula is given as:…”

Section: Inference Enginementioning

confidence: 99%

“…For instance, if the linguistic term "large" of the criterion "time for diagnosis" (Fig. 6) has the most probable value 1 between 20 and 22 min, and this value gradually decreases to 0 within 5 min, the membership function of this linguistic term is written in (15,20,22,27). Similarly, the rest of the linguistic terms for the other criteria can be defined in the same way (Figs 7 and 8).…”

Section: Fuzzy Group Multiple Attribute Decision Makingmentioning

confidence: 99%

See 1 more Smart Citation

An Efficient Expert System for Machine Fault Diagnosis

Liu

2003

The International Journal of Advanced Manufacturing Technology

View full text Add to dashboard Cite

An efficient expert system for machine fault diagnosis is developed. A new search method is proposed in this system to improve the efficiency of the diagnostic process. First of all, a diagnostic tree (a decision tree) is built by domain experts according to the functions of the devices in the machine. Then, the diagnostic priorities of nodes (devices) in the tree are determined based on a fuzzy group multiple attribute decision making method. A meta knowledge base for fault diagnosis is generated automatically based on the determined priorities to guide the diagnostic process. After that, a domain knowledge base that hypothesises possible faults for each device in the tree is generated by domain experts and/or manuals. At last, the inference process starts based on the meta knowledge base and hypothesises which device is the possible cause of failure. To validate the system performance, an illustrative example (VCR troubleshooting) is presented for demonstration purposes.

show abstract

Section: Inference Enginementioning

confidence: 99%

Section: Fuzzy Group Multiple Attribute Decision Makingmentioning

confidence: 99%

An Efficient Expert System for Machine Fault Diagnosis

Liu

2003

The International Journal of Advanced Manufacturing Technology

View full text Add to dashboard Cite

show abstract

“…This is because these methods can be used to recognize the variability arising from three different sources of errors, namely, analytical test errors, sampling errors and time errors, in addition to the variability in the true concentration [4]. The Bayesian methods can also be used to significantly increase the precision and the accuracy of the test methods used in a given environmental laboratory [5].…”

Section: Introductionmentioning

confidence: 98%

Use of a Bayesian network decision tool for water quality assessment

Al-Chalabi

Shihab

2008

2008 International Symposium on Information Technology

View full text Add to dashboard Cite

Groundwater resources in Oman are considered very precious and play a great role in the economical development. However, groundwater contamination is one of the major concerns facing the country and, therefore, it needs an accurate measurement technique. In this work, the Bayesian Technique is applied to groundwater quality data sets obtained from various locations in the Salalah area to the south of Oman. This technique emphasizes not only theprobabilistic dependencies between pollutants but also the precision and the accuracy of the tested methods used by environmental laboratories. First, we present a new technique for data preprocessing. Then we describe the network models we developed, as l1ell as the methods used to build these models. Various challenges, such as acquiring groundwater datasets, identifying pollutants and anticipating potential problem contaminants, are addressed. Finally, we present the results of applications of these models.

show abstract

“…Given their solid theoretical foundation, flexible inference capability and convenient decision support mechanism, BNs have a great potential in the modeling of WWTSs [1]. Scholars have attempted to diagnose WWTSs using BN tools [6,7]. In the present work, we proposes a method for modelling a WWTS based on MSBR via BNs.…”

Section: Introductionmentioning

confidence: 99%

A Methodology for modeling Wastewater Treatment Process based on Uncertainty Theory

Li¹,

Tao²,

Xiao³

2017

Proceedings of the 2017 6th International Conference on Energy and Environmental Protection (ICEEP 2017)

View full text Add to dashboard Cite

Abstract. Wastewater treatment is a complicated dynamic process affected by microbial, chemical and physical factors. These variables are always uncertain. Due to the complex biological reaction mechanisms, the highly time-varying and multivariable aspects, the traditional analysis method in the description and simulation of complex reaction process and mechanism of wastewater treatment encountered challenges. However, we can use uncertainty theory to mine the rules behind the data and find the relationship between them. The Bayesian network is a powerful knowledge representation tool that deals explicitly with uncertainty. This paper employed the Bayesian network to make active exploration on the modeling of wastewater treatment system. An example is given to illustrate how to build a BN based sewage treatment system model.

show abstract

Rule-based versus probabilistic approaches to the diagnosis of faults in wastewater treatment processes

Cited by 24 publications

References 3 publications

An Efficient Expert System for Machine Fault Diagnosis

An Efficient Expert System for Machine Fault Diagnosis

Use of a Bayesian network decision tool for water quality assessment

A Methodology for modeling Wastewater Treatment Process based on Uncertainty Theory

Contact Info

Product

Resources

About