Fault detection and isolation in the challenging Tennessee Eastman process by using image processing techniques

Hajihosseini, Payman; Anzehaee, Mohammad Mousavi; Behnam, Behzad

doi:10.1016/j.isatra.2018.05.002

Cited by 24 publications

(10 citation statements)

References 30 publications

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“…21 type faults and one normal status has been simulated, which can be used to verify the feasibility and effectiveness of fault detection and diagnosis on process monitoring. More details of process description have been elaborated in [26]- [30].…”

Section: Process Descriptionmentioning

confidence: 99%

Spatial-temporal associations representation and application for process monitoring using graph convolution neural network

Ren¹,

Yang²,

Liang³

et al. 2022

Preprint

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Industrial process data reflects the dynamic changes of operation conditions, which mainly refer to the irregular changes in the dynamic associations between different variables in different time. And this related associations knowledge for process monitoring is often implicit in these dynamic monitoring data which always have richer operation condition information and have not been paid enough attention in current research. To this end, a new process monitoring method based on spatial-based graph convolution neural network (SGCN) is proposed to describe the characteristics of the dynamic associations which can be used to represent the operation status over time. Spatia-temporal graphs are firstly defined, which can be used to represent the characteristics of node attributes (dynamic edge features) dynamically changing with time. Then, the associations between monitoring variables at a certain time can be considered as the node attributes to define a snapshot of the static graph network at the certain time. Finally, the snapshot containing graph structure and node attributes is used as model inputs which are processed to implement graph classification by spatial-based convolution graph neural network with aggregate and readout steps. The feasibility and applicability of this proposed method are demonstrated by our experimental results of benchmark and practical case application.

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Section: Process Descriptionmentioning

confidence: 99%

Spatial-temporal associations representation and application for process monitoring using graph convolution neural network

Ren¹,

Yang²,

Liang³

et al. 2022

Preprint

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“…Notably, performance quality control of large-scale electromechanical systems contains four parts: monitoring, evaluation, fault root cause tracing, and supportability. Detailed information on and the technologies that use the first three parts can be found in the literature [2,[24][25][26][27][28][29][30]. As shown in FIGURE 1, monitoring forms the foundation of supportability, and both evaluation and fault root cause tracing trigger dynamic supportability activities.…”

Section: Contents Of the Dynamic Supportability Mechanism Of Perfmentioning

confidence: 99%

Condition-Based Dynamic Supportability Mechanism for the Performance Quality of Large-Scale Electromechanical Systems

Wang

Gao

et al. 2020

IEEE Access

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Scientific and effective supportability mechanisms have a profound impact on large-scale electromechanical systems and improve their performance quality, while decreasing costs. However, the contradiction between the static supportability strategy and time-varying performance quality conditions remains not sufficiently resolved. To solve this problem, this paper proposes a performance quality condition-based dynamic supportability mechanism. First, model-driven risk identification and control chart-based pattern recognition were used to trigger the dynamic allocation of the limit supportability resource. Second, the performance quality mode and effect analysis and the multiagent collaborative chain were defined to identify the resource lists of maintenance activities. Third, a general control framework was constructed to evaluate the compliance and applicability of maintenance activities. This study formulated a dynamic supportability mechanism for performance quality with a closed loop of activity triggering, resource guarantee, and maintenance effectiveness evaluation. As an expansion and improvement of condition-based maintenance, the dynamic supportability mechanism overcomes the several drawbacks of the existing supportability mechanisms of large-scale electromechanical systems. The proposed mechanism also advances the integration of management and technology in comprehensive supportability. Therefore, the proposed mechanism and methods can be flexibly and efficiently used for the dynamic supportability of electromechanical systems. Moreover, this study provides insights into risk identification, macro pattern recognition and process control and can be used for other engineering applications.

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“…Image processing is also present in different areas of engineering, such as electrical engineering, with the detection faults found in electric motors [22], electrical capacitance tomography [23] or in the classification of solder joints in surfacemount devices [24]. It can also be found in chemical engineering, with fault detection and isolation of the Tennessee Eastman process [25] or in the ceramic and tile industry with surface defect detection [20]. Although in reduced numbers when compared with other approaches, image processing has also been used in solar energy.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection in PV Tracking Systems Using an Image Processing Algorithm Based on PCA

Amaral

Pires

2021

Energies

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Photovoltaic power plants nowadays play an important role in the context of energy generation based on renewable sources. With the purpose of obtaining maximum efficiency, the PV modules of these power plants are installed in trackers. However, the mobile structure of the trackers is subject to faults, which can compromise the desired perpendicular position between the PV modules and the brightest point in the sky. So, the diagnosis of a fault in the trackers is fundamental to ensure the maximum energy production. Approaches based on sensors and statistical methods have been researched but they are expensive and time consuming. To overcome these problems, a new method is proposed for the fault diagnosis in the trackers of the PV systems based on a machine learning approach. In this type of approach the developed method can be classified into two major categories: supervised and unsupervised. In accordance with this, to implement the desired fault diagnosis, an unsupervised method based on a new image processing algorithm to determine the PV slopes is proposed. The fault detection is obtained comparing the slopes of several modules. This algorithm is based on a new image processing approach in which principal component analysis (PCA) is used. Instead of using the PCA to reduce the data dimension, as is usual, it is proposed to use it to determine the slope of an object. The use of the proposed approach presents several benefits, namely, avoiding the use of a wide range of data and specific sensors, fast detection and reliability even with incomplete images due to reflections and other problems. Based on this algorithm, a deviation index is also proposed that will be used to discriminate the panel(s) under fault. Several test cases are used to test and validate the proposed approach. From the obtained results, it is possible to verify that the PCA can successfully be adapted and used in image processing algorithms to determine the slope of the PV modules and so effectively detect a fault in the tracker, even when there are incomplete parts of an object in the image.

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Fault detection and isolation in the challenging Tennessee Eastman process by using image processing techniques

Cited by 24 publications

References 30 publications

Spatial-temporal associations representation and application for process monitoring using graph convolution neural network

Spatial-temporal associations representation and application for process monitoring using graph convolution neural network

Condition-Based Dynamic Supportability Mechanism for the Performance Quality of Large-Scale Electromechanical Systems

Fault Detection in PV Tracking Systems Using an Image Processing Algorithm Based on PCA

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