A comparison study of basic data-driven fault diagnosis methods for variable refrigerant flow system

Zhou, Zhongshi; Li, Guannan; Wang, Jiangyu; Chen, Huanxin; Zhong, Hanlu; Cao, Zihan

doi:10.1016/j.enbuild.2020.110232

Cited by 40 publications

(11 citation statements)

References 55 publications

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“…Research on data-driven approaches, for fault diagnosis of HVAC chillers, is discussed by [31,32]. Data-driven methods are used for fault detection and diagnosis in air handling units in [12,33] compares basic data-driven fault diagnosis methods for a system with variable refrigerant.…”

Section: Data-driven Approachesmentioning

confidence: 99%

Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach

et al. 2021

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Heating, ventilation, and air conditioning (HVAC) systems installed in a passenger train carriage are critical systems, whose failures can affect people or the environment. This, together with restrictive regulations, results in the replacement of critical components in initial stages of degradation, as well as a lack of data on advanced stages of degradation. This paper proposes a hybrid model-based approach (HyMA) to overcome the lack of failure data on a HVAC system installed in a passenger train carriage. The proposed HyMA combines physics-based models with data-driven models to deploy diagnostic and prognostic processes for a complex and critical system. The physics-based model generates data on healthy and faulty working conditions; the faults are generated in different levels of degradation and can appear individually or together. A fusion of synthetic data and measured data is used to train, validate, and test the proposed hybrid model (HyM) for fault detection and diagnostics (FDD) of the HVAC system. The model obtains an accuracy of 92.60%. In addition, the physics-based model generates run-to-failure data for the HVAC air filter to develop a remaining useful life (RUL) prediction model, the RUL estimations performed obtained an accuracy in the range of 95.21–97.80% Both models obtain a remarkable accuracy. The development presented will result in a tool which provides relevant information on the health state of the HVAC system, extends its useful life, reduces its life cycle cost, and improves its reliability and availability; thus enhancing the sustainability of the system.

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Section: Data-driven Approachesmentioning

confidence: 99%

Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach

et al. 2021

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“…They do not need a mathematical model of the process. The data-driven fault diagnosis techniques are significantly developed in the previous decade [6]- [10]. Among the data-driven techniques, the multivariate statistical approach is commonly applied for batch processes.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Fault Diagnostics for Industrial Processes: An Application to Penicillin Fermentation Process

et al. 2021

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We consider the problem of fault detection and isolation for the penicillin fermentation process. A penicillin fermentation process is a highly complex and nonlinear dynamic process with batch processing. A data-driven approach is utilized for fault diagnostics due to the availability of huge batch processing data and the unavailability of an analytical model. To address the non-linearity, a subspaceaided parity-based residual generation technique is proposed by using a just-in-time learning approach. For the just-in-time learning approach, the most similar data samples are selected from the database for incoming test samples and a subspace aided parity-based residual is generated using these samples. The designed fault detection technique is implemented for the penicillin fermentation process to demonstrate real-time health monitoring of the process under sensor noise and process disturbances. Two sensor faults and an actuator fault are considered and successfully detected using the proposed technique. Further, a fault isolation approach is developed to isolate these faults and their location has been identified. A case study is given to show the improvement offered by the proposed technique for the fault detection rate and minimization of the false alarm rate as compared to the existing techniques for the penicillin fermentation process.INDEX TERMS Data-driven, fault diagnostics, process monitoring, subspace identification, just-in-time learning, penicillin fermentation processThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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“…As an industry with high energy consumption, the construction industry has become a key area for energy conservation and emission reduction (Li et al, 2019a;Daneshvar et al, 2020). In particular, the energy waste caused by the aging of heating, ventilation, and air conditioning (HVAC) systems as well as equipment failure accounts for 15-30% of the total energy consumption of buildings (Zhou et al, 2020) and has become a "severe disaster area" in terms of building energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…Chiller is the equipment with the largest energy consumption in HVAC system. Relevant research shows that timely troubleshooting of chiller can effectively reduce energy consumption by 20-50% (Zhou et al, 2020). Therefore, rapid and accurate judgment of chiller operation state is an important basis for ensuring safe and stable operation of chiller and saving energy.…”

Section: Introductionmentioning

confidence: 99%

Chiller Fault Diagnosis Based on Automatic Machine Learning

Tian

Wang

et al. 2021

Front. Energy Res.

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Intelligent diagnosis is an important means of ensuring the safe and stable operation of chillers driven by big data. To address the problems of input feature redundancy in intelligent diagnosis and reliance on human intervention in the selection of model parameters, a chiller fault diagnosis method was developed in this study based on automatic machine learning. Firstly, the improved max-relevance and min-redundancy algorithm was used to extract important feature information effectively and automatically from the training data. Then, the long short-term memory (LSTM) model was used to mine the temporal correlation between data, and the genetic algorithm was employed to train and optimize the model to obtain the optimal neural network architecture and hyperparameter configuration. Finally, a transient co-simulation platform for building chillers based on MATLAB as well as the Engineering Equation Solver was built, and the effectiveness of the proposed method was verified using a dynamic simulation dataset. The experimental results showed that, compared with traditional machine learning methods such as the recurrent neural network, back propagation neural network, and support vector machine methods, the proposed automatic machine learning algorithm based on LSTM provides significant performance improvement in cases of low fault severity and complex faults, verifying the effectiveness and superiority of this method.

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A comparison study of basic data-driven fault diagnosis methods for variable refrigerant flow system

Cited by 40 publications

References 55 publications

Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach

Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach

Data-Driven Fault Diagnostics for Industrial Processes: An Application to Penicillin Fermentation Process

Chiller Fault Diagnosis Based on Automatic Machine Learning

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