An analysis of process fault diagnosis methods from safety perspectives

Arunthavanathan, Rajeevan; Khan, Faisal; Ahmed, Salim; Imtiaz, Syed

doi:10.1016/j.compchemeng.2020.107197

Cited by 95 publications

(29 citation statements)

References 184 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Over the last decades, the increase in the performance of equipment in terms of production quality and gain in productivity was accompanied by the complexity of the equipment. However, the presence of abnormal changes due to actuator or system or sensor faults can degrade system performances, hence the need to integrate fault detection and diagnosis (FDD) tools [1], [2] to maintain, for a long time, the desired performance of the whole system in various sectors. In particular, FDD has a very important role in monitoring the behavior of system variables and revealing faults, and it is performed based on the relative information to the system and its equipment.…”

Section: Introductionmentioning

confidence: 99%

“…There have been many methods of FDD [2], [11], [12] which can be classified into signal-based approaches [13], knowledge-based approaches [12], [14], and process model-based approaches [12], [13] which contain state observer-based method representing an analytical method having achieved several results in this field, and which depends on the mathematical model of the studied system without needing other components. Many publications have been interested in the design of observers for FDD [15]- [24] and have presented fruitful results.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

State and fault estimation based on fuzzy observer for a class of Takagi-Sugeno singular models

Ouarid

Essabre

Assoudi

et al. 2022

IJEECS

View full text Add to dashboard Cite

Singular nonlinear systems have received wide attention in recent years, and can be found in various applications of engineering practice. On the basis of the Takagi-Sugeno (T-S) formalism, which represents a powerful tool allowing the study and the treatment of nonlinear systems, many control and diagnostic problems have been treated in the literature. In this work, we aim to present a new approach making it possible to estimate simultaneously both non-measurable states and unknown faults in the actuators and sensors for a class of continuous-time Takagi-Sugeno singular model (CTSSM). Firstly, the considered class of CTSSM is represented in the case of premise variables which are non-measurable, and is subjected to actuator and sensor faults. Secondly, the suggested observer is synthesized based on the decomposition approach. Next, the observer’s gain matrices are determined using the Lyapunov theory and the constraints are defined as linear matrix inequalities (LMIs). Finally, a numerical simulation on an application example is given to demonstrate the usefulness and the good performance of the proposed dynamic system.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

State and fault estimation based on fuzzy observer for a class of Takagi-Sugeno singular models

Ouarid

Essabre

Assoudi

et al. 2022

IJEECS

View full text Add to dashboard Cite

show abstract

“…Since industry became a separate category from social production, data collection and use in industrial production has gradually increased. In this context, data-driven multivariate statistical process monitoring (MSPM) methods have developed leaps and bounds [1,2], where principal component analysis (PCA) methods are the most widely used [3][4][5][6]. However, there are cases where PCA-based fault detection methods do not perform well.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fault Detection Scheme Based on Mutual k-Nearest Neighbor Method: Application on the Industrial Processes with Outliers

Wang

Zhou

et al. 2022

Processes

View full text Add to dashboard Cite

The k-nearest neighbor (kNN) method only uses samples’ paired distance to perform fault detection. It can overcome the nonlinearity, multimodality, and non-Gaussianity of process data. However, the nearest neighbors found by kNN on a data set containing a lot of outliers or noises may not be actual or trustworthy neighbors but a kind of pseudo neighbor, which will degrade process monitoring performance. This paper presents a new fault detection scheme using the mutual k-nearest neighbor (MkNN) method to solve this problem. The primary characteristic of our approach is that the calculation of the distance statistics for process monitoring uses MkNN rule instead of kNN. The advantage of the proposed approach is that the influence of outliers in the training data is eliminated, and the fault samples without MkNNs can be directly detected, which improves the performance of fault detection. In addition, the mutual protection phenomenon of outliers is explored. The numerical examples and Tenessee Eastman process illustrate the effectiveness of the proposed method.

show abstract

“…While these failures may not be serious in the early stages, the performance of the system has indeed been degraded [ 4 , 5 ]. Therefore, the early detections and identifications of any potential anomalies and failures are essential, as they avoid dangers for high-speed train operation [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Brake Train Based on Multi-Sensor Data Fusion

Jin

Xie

et al. 2021

Sensors

View full text Add to dashboard Cite

In this paper, a fault diagnosis method is proposed based on multi-sensor fusion information for a single fault and composite fault of train braking systems. Firstly, the single mass model of the train brake is established based on operating environment. Then, the pre-allocation and linear-weighted summation criterion are proposed to fuse the monitoring data. Finally, based on the improved expectation maximization, the braking modes and braking parameters are identified, and the braking faults are diagnosed in real time. The simulation results show that the braking parameters of systems can be effectively identified, and the braking faults can be diagnosed accurately based on the identification results. Even if the monitoring data are missing or abnormal, compared with the maximum fusion, the accuracies of parameter identifications and fault diagnoses can still meet the needs of the actual systems, and the effectiveness and robustness of the method can be verified.

show abstract

An analysis of process fault diagnosis methods from safety perspectives

Cited by 95 publications

References 184 publications

State and fault estimation based on fuzzy observer for a class of Takagi-Sugeno singular models

State and fault estimation based on fuzzy observer for a class of Takagi-Sugeno singular models

A Novel Fault Detection Scheme Based on Mutual k-Nearest Neighbor Method: Application on the Industrial Processes with Outliers

Fault Diagnosis of Brake Train Based on Multi-Sensor Data Fusion

Contact Info

Product

Resources

About