Fault detection and diagnosis of chillers using Bayesian network merged distance rejection and multi-source non-sensor information

Wang, Zhanwei; Wang, Zhiwei; He, Suowei; Gu, Xiaowei; Yan, Zeng

doi:10.1016/j.apenergy.2016.11.130

Cited by 110 publications

(26 citation statements)

References 34 publications

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“…They offered a novel data‐driven FDI approach based on Fuzzy‐Bayesian inference for detection and diagnosis of sensor faults in a standard wind turbine benchmark. Wang et al applied fault detection and diagnosis (FDD) techniques for chillers to reduce building energy consumption and to enhance the energy efficiency of buildings. They proposed a novel chiller FDD method based on BN, distance rejection, and multisource nonsensor mutual information (MI).…”

Section: State Of the Artmentioning

confidence: 99%

Soft sensor design and fault detection using Bayesian network and probabilistic principal component analysis

Mohammadi

Zarghami

Lefebvre

et al. 2019

J Adv Manuf & Process

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Bayesian network (BN) and probabilistic principal component analysis (PPCA) are powerful tools in artificial intelligence. In this study, two intelligent soft sensors, designed based on BN and PPCA, were proposed for fault detection and data prediction in chemical processes. A gas sweetening process was considered as a case study in which the existing data for H 2 S concentration in sweet gas stream were incomplete. In order to detect faults during the operation, a soft sensor was designed using BN and PPCA for predicting H 2 S concentration in the sweet gas stream. Then, another soft sensor was developed based on the BN for fault detection, considering the Gaussian mixture model with hidden nodes. The Tennessee-Eastman challenge process was used to assess the efficiency of the fault detection method. Results showed that the predicted values of the soft sensors are very close to real data. The fault occurring in this process was detected in early stages, demonstrating the good performance of the proposed fault detection system. It was also shown that the performance of the BN is better than the PPCA in prediction of incomplete data. Moreover, the confidence interval can be evaluated for the predicted values when using the BN. An uncertainty analysis was performed to assess the quality of predicted data, and it was observed that the error magnitude of predicted data is smaller when using the BN compared with the PPCA. In particular, the results showed that the BN model is capable of estimating H 2 S concentration with nearly 96.1% accuracy, whereas the accuracy of the PCA-based method was 93.2%. K E Y W O R D SBayesian network, fault detection, gas sweetening process, probabilistic principal component analysis, soft sensor, Tennessee-Eastman challenge process

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Section: State Of the Artmentioning

confidence: 99%

Soft sensor design and fault detection using Bayesian network and probabilistic principal component analysis

Mohammadi

Zarghami

Lefebvre

et al. 2019

J Adv Manuf & Process

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“…Proceedings of SIMS 2020 Virtual, Finland, 22-24 September 2020 fully shown on a chiller (Wang et al, 2017), a heat pump (Cai et al, 2014) and a fuel cell (Riascos et al, 2007). A sensor fault detection work was published for a medical body sensor network (Zhang et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Sensor fault detection with Bayesian networks

Iplik¹,

Aslanidou²,

Kyprianidis³

2021

Linköping Electronic Conference Proceedings

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Several sensors are installed in the majority of chemical reactors and storage tanks to monitor temperature profiles for safety and decision-making processes such as heat demand or flow rate calculations. These sensors fail occasionally and generate erroneous measurement data that need to be detected and excluded from the calculations. However, due to the high number of process variables displayed in the chemical plants, this task is not trivial. In this work, a Bayesian network approach to detect faulty temperature sensors is proposed. By comparing the sensor measurements with each other, the faulty sensor is detected. A modular approach is preferred, and networks are created for 10 K temperature intervals to increase flexibility and sensitivity. Created networks can be adjusted for the operating temperature ranges; hence, they can be used for any catalyst and entire life cycle. The developed method is demonstrated on an industrial scale hydrocracker unit with 92 sensor couples installed in a series of reactors. From the investigated sensors, 16 of them showed a greater difference than the 2 K threshold chosen for the fault. In addition to that, 13 sensors showed an increasing temperature difference that may lead to a fault. Two scenarios were created to calculate the energy loss due to a faulty measurement, and a 5.5 K offset error was found to cause a 5.79 TJ energy loss every year for a small scale hydrocracker.

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“…Many researchers also explored the BN-based approach for fault isolation and multiple-fault diagnosis of different machinery systems [16][17][18], e.g. centrifugal compressors [19], chillers [20,21], chemical processes [22] and gear pumps [23]. Cai and his research team carried out a series of works on machinery fault diagnosis using BNs and the extension over the years [9,[24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Exploiting Bayesian networks for fault isolation: A diagnostic case study of diesel fuel injection system

Wang

Stetsyuk

et al. 2019

ISA Transactions

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Fault isolation is known to be a challenging problem in machinery troubleshooting. It is not only because the isolation of multiple faults contains considerable number of uncertainties due to the strong correlation and coupling between different faults, but often massive prior knowledge is needed as well. This paper presents a Bayesian network-based approach for fault isolation in the presence of the uncertainties. Various faults and symptoms are parameterized using state variables, or the so-called nodes in Bayesian networks (BNs). Probabilistically causality between a fault and a symptom and its quantization are described respectively by a directed edge and conditional probability. To reduce the qualitative and quantitative knowledge needed, particular considerations are given to the simplification of Bayesian networks structures and conditional probability expressions using rough sets and noisy-OR/MAX model, respectively. By adopting the simplified approach, symptoms under multiple-fault are decoupled into the ones under every single fault, while the quantity of the conditional probabilities is simplified into the linear form of the faults quantity. Prior knowledge needed in Bayesian network-based diagnostic model is reduced significantly, which decreases the complexity in establishing and applying this diagnosis model. The computational efficiency is improved accordingly in the simplified BN model, after eliminating the redundant symptoms. The fault isolation methodology is illustrated through an example of diesel engine fuel injection system to verify the developed model.

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Fault detection and diagnosis of chillers using Bayesian network merged distance rejection and multi-source non-sensor information

Cited by 110 publications

References 34 publications

Soft sensor design and fault detection using Bayesian network and probabilistic principal component analysis

Soft sensor design and fault detection using Bayesian network and probabilistic principal component analysis

Sensor fault detection with Bayesian networks

Exploiting Bayesian networks for fault isolation: A diagnostic case study of diesel fuel injection system

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