Actuator Fault Diagnosis in a Heat Exchanger based on Classifiers - A Comparative Study

Tudón-Martínez, Juan C.

doi:10.1016/j.ifacol.2015.09.691

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…The study [6] achieved 90% accuracy with k-NN but the ensemble-based model provided the best results in the study conducted by [15]. So the present research uses state-of-theart machine learning algorithms for the diagnosis of intermittent faults from the sensor signals.…”

Section: A Objectives Of the Studymentioning

confidence: 88%

“…The model accurately predicted the bearing faults from the vibration signal and classified the defects from the experimental data [5]. 1100 data vectors from an experimental heat exchanger system are used in [6] to train ANN for fault classification and fault isolation. K-nearest neighbours(k-NN) based model was found to be the best among all the tested classifiers with 90% of correct detections [6].…”

Section: A K-nn For Fault Diagnosismentioning

confidence: 99%

“…1100 data vectors from an experimental heat exchanger system are used in [6] to train ANN for fault classification and fault isolation. K-nearest neighbours(k-NN) based model was found to be the best among all the tested classifiers with 90% of correct detections [6]. The performance of different fault detection machine learning algorithms for wireless sensor networks was compared by [7].…”

Section: A K-nn For Fault Diagnosismentioning

confidence: 99%

“…In [4] and [5] K-NN was used for fault diagnosis in bearings. K-NN was also used in heat exchangers by [6], in wireless sensors by [7], in multimode by [8], and in chiller systems by [9]. Machine learning algorithms like Support Vector Machine(SVM) are used by [10]- [14] and ensemble by [15]- [19] for fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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Intermittent Fault Diagnosis in Sensors using Artificial Neural Network: A Comparative Study

Mishra¹,

Mohanty²

2022

2022 IEEE International Conference on Current Development in Engineering and Technology (CCET)

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Processing algorithms and processing hardware have developed at a very rapid pace in the last three decades. Sensors play a vital role in the condition monitoring of various components of a manufacturing system and to measure different variables that may affect the performance of these systems. Real-time collection of data, processing of the collected data, and decision-making are challenges. Intermittent fault diagnosis is difficult as it occurs randomly and does not follow a pattern. Non-availability of a suitably labeled dataset for testing the algorithms is also a challenge. Keeping these things in mind in the present research fault-injected sensor signals are developed from the publicly available Intel Lab dataset for temperature and light sensor signals. With the help of the proposed algorithm, a Matlab code for intermittent fault injection is developed. The performances of the various machine-learning algorithms extensively used in literature are also compared with the help of accuracy, precision, recall, and F1 score values. The performances of the artificial neural networks are compared with SVM, Ensemble, and k-NN in classifying various intermittent fault modes using the classification learner application of Matlab. The trained bilayer neural network has achieved an F1 score of 0.89 which is the highest among the other tested machine learning algorithms.

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Section: A Objectives Of the Studymentioning

confidence: 88%

Section: A K-nn For Fault Diagnosismentioning

confidence: 99%

Section: A K-nn For Fault Diagnosismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Intermittent Fault Diagnosis in Sensors using Artificial Neural Network: A Comparative Study

Mishra¹,

Mohanty²

2022

2022 IEEE International Conference on Current Development in Engineering and Technology (CCET)

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“…Further, for induction motor fault diagnosis, KNN has been applied by Nguyen and Lee 129. A fault detection and isolation method have also been developed for an industrial shell and tube heat exchanger by Tudón‐Martínez and Morales‐Menendez 130.…”

Section: Data Driven Approachesmentioning

confidence: 99%

A Review on Data‐Driven Learning Approaches for Fault Detection and Diagnosis in Chemical Processes

et al. 2021

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Fault detection and diagnosis for process plants has been an active area of research for many years. This review presents a concise overview on supervised and unsupervised data‐driven approaches for fault detection and diagnosis in chemical processes. Methods based on supervised and unsupervised data‐driven techniques are reviewed, and the challenges in the field of fault detection and diagnosis have also been highlighted. It is observed that most of the data‐driven approaches are application specific, in that no single method can be used to obtain a generalized solution for nearly all purposes. The methods reviewed differ significantly from one to the other, and hence it is difficult to generalize any key similarity. The majority of the works proposed in the literature focused mainly on single fault detection, and do not cover the root‐cause diagnosis of the detected faults. In cases where both detection and diagnosis are performed, the focus is mainly for a single fault. In addition, majority of the articles do not extend to the diagnosis of the root cause for multiple and simultaneous faults.

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Statistical approaches and artificial neural networks for process monitoring

Alauddin

Arunthavanathan

Amin

et al. 2022

Methods in Chemical Process Safety

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Actuator Fault Diagnosis in a Heat Exchanger based on Classifiers - A Comparative Study

Cited by 6 publications

References 19 publications

Intermittent Fault Diagnosis in Sensors using Artificial Neural Network: A Comparative Study

Intermittent Fault Diagnosis in Sensors using Artificial Neural Network: A Comparative Study

A Review on Data‐Driven Learning Approaches for Fault Detection and Diagnosis in Chemical Processes

Statistical approaches and artificial neural networks for process monitoring

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