Comparing a knowledge-based and a data-driven method in querying data streams for system fault detection: A hydraulic drive system application

Alzghoul, Ahmad; Backe, Björn; Löfstrand, Magnus; Byström, Arne; Liljedahl, Bengt

doi:10.1016/j.compind.2014.06.003

Cited by 56 publications

(34 citation statements)

References 17 publications

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“…One of the most essential parts of risk in analyzing system reliability and safety is the risk analysis procedure [8][9][10]. In general, the novel methods are mainly classified into the knowledge-based and data-driven approaches for risk and reliability analysis and prediction under various situations [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Sustainable Food Production: An Intelligent Fault Diagnosis Framework for Analyzing the Risk of Critical Processes

et al. 2022

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Fault diagnosis and prognosis methods are the most useful tools for risk and reliability analysis in food processing systems. Proactive diagnosis techniques such as failure mode and effect analysis (FMEA) are important for detecting all probable failures and facilitating the risk analysis process. However, significant uncertainties exist in the classical-FMEA when it comes to ranking the risk priority numbers (RPNs) of failure modes. Such uncertainties may have an impact on the food sector’s operational safety and maintenance decisions. To address these issues, this research provides a unique FMEA framework for risk analysis within an edible oil purification facility that is based on certain well-known intelligent models. Fuzzy inference systems (FIS), adaptive neuro-fuzzy inference systems (ANFIS), and support vector machine (SVM) models are among those used. The findings of the comparison of the proposed FMEA framework with the classical model revealed that intelligent strategies were more effective in ranking the RPNs of failure modes. Based on the performance criteria, it was discovered that the SVM algorithm classifies the failure modes more accurately and with fewer errors., e.g., RMSE = 7.30 and MAPE = 13.19 with that of other intelligent techniques. Hence, a sensitivity FMEA analysis based on the SVM algorithm was performed to put forward suitable maintenance actions to upgrade the reliability and safety within food processing lines.

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Section: Introductionmentioning

confidence: 99%

Sustainable Food Production: An Intelligent Fault Diagnosis Framework for Analyzing the Risk of Critical Processes

et al. 2022

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“…Presently, model-based FDA using observers and estimators have great significance as system states can be estimated by engaging the system-dynamics effectively [9][10][11]. In contrast, data-driven approaches utilize high dimensional results to make any decision [12]. For example, an innovative modelfree neural network (NN)-based active fault-tolerant control scheme was proposed in [8].…”

Section: Introductionmentioning

confidence: 99%

An Unknown Input Observer–EFIR Combined Estimator for Electrohydraulic Actuator in Sensor Fault-Tolerant Control Application

Nahian

Dinh

Dao

et al. 2020

IEEE/ASME Trans. Mechatron.

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“…When the detailed mathematical model cannot be reached, and when the number of inputs, outputs, and states of a system is logically limited, the knowledge-based method will yield the best results [ 4 ]. To detect faults, the data-driven methods use products’ life-cycle data, which they are not dependent on the first-principles [ 5 , 6 ]. Hence, data-driven methods can be used for large-scale and complex systems, which are inexpensive as well [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…As a consequence, a multivariate statistical approach is needed when the number of variables or dimensions of an industrial problem is beyond one value. One of the data-driven multivariate statistical tools is the quality control charts by the principal component analysis approach (PCA) to detect an abnormal behavior [ 6 , 8 ]. However, in addition to their high dimensionality, data reported to solve sophisticated industrial, health care, IT, or economic problems have characteristics such as autocorrelation and non-stationary nature [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

An improved approach for fault detection by simultaneous overcoming of high-dimensionality, autocorrelation, and time-variability

2020

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The control charts with the Principal Component Analysis (PCA) approach and its extension are among the data-driven methods for process monitoring and the detection of faults. Industrial processing data involves complexities such as high dimensionality, auto-correlation, and non-stationary which may occur simultaneously. An efficient fault detection technique is an approach that is robust against data training, sensitive to all the feasible faults of the process, and agile to the detection of the faults. To date, approaches such as the recursive PCA (RPCA) model and the moving-window PCA (MWPCA) model have been proposed when data is high-dimensional and non-stationary or dynamic PCA (DPCA) model and its extension have been suggested for autocorrelation data. But, using the techniques listed without considering all aspects of the process data increases fault detection indicators such as false alarm rate (FAR), delay time detection (DTD), and confuses the operator or causes adverse consequences. A new PCA monitoring method is proposed in this study, which can simultaneously reduce the impact of high-dimensionality, non-stationary, and autocorrelation properties. This technique utilizes DPCA property to decrease the effect of autocorrelation and adaptive behavior of MWPCA to control non-stationary characteristics. The proposed approach has been tested on the Tennessee Eastman Process (TEP). The findings suggest that the proposed approach is capable of detecting various forms of faults and comparing attempts to improve the detection of fault indicators with other approaches. The empirical application of the proposed approach has been implemented on a turbine exit temperature (TET). The results demonstrate that the proposed approach has detected a real fault successfully.

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Comparing a knowledge-based and a data-driven method in querying data streams for system fault detection: A hydraulic drive system application

Cited by 56 publications

References 17 publications

Sustainable Food Production: An Intelligent Fault Diagnosis Framework for Analyzing the Risk of Critical Processes

Sustainable Food Production: An Intelligent Fault Diagnosis Framework for Analyzing the Risk of Critical Processes

An Unknown Input Observer–EFIR Combined Estimator for Electrohydraulic Actuator in Sensor Fault-Tolerant Control Application

An improved approach for fault detection by simultaneous overcoming of high-dimensionality, autocorrelation, and time-variability

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