An Experimental Study on Condition Diagnosis for Thrust Bearings in Oscillating Water Column Type Wave Power Systems

Kim, Tae-Wook; Oh, Jaewon; Min, Cheon-Hong; Hwang, Se-Yun; Kim, Minseok; Lee, Jang-Hyun

doi:10.3390/s21020457

Cited by 6 publications

(7 citation statements)

References 30 publications

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“…In this way, features potentially representing faults are extracted from the sensor data. After that, we reduce the dimension of the features, and build the fault classification map represented by the features through several statistical methods, e.g., principal component analysis (PCA) and the Bayesian classifier [25][26][27]. In the classifier learning process, previous fault history information and normal operation status information are used as the learning data of the fault classifier in the supervised learning scheme.…”

Section: Detailed Procedures For Fault Diagnosismentioning

confidence: 99%

“…Sci. 2021, 11, x FOR PEER REVIEW 4 of 15 statistical methods, e.g., principal component analysis (PCA) and the Bayesian classifier [25][26][27]. In the classifier learning process, previous fault history information and normal operation status information are used as the learning data of the fault classifier in the supervised learning scheme.…”

Section: Detailed Procedures For Fault Diagnosismentioning

confidence: 99%

“…Dimensional reduction of the sensor data is a data processing method by focusing on representing data with minimal number of dimensions while keeping the fundamental properties of the data from being lost. PCA is a method that is applied to sensor data analysis and processing of multiple data in order to reduce the data dimensions [26,27]. In this study, to minimize the dimensionality of the features and classify the fault condition clearly, two principal components having the high Eigen values are selected, as shown in Figure 4.…”

Section: Dimensionality Reductionmentioning

confidence: 99%

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Application of PCA and Classification for Fault Diagnosis of MAB Installed in Petrochemical Plant Process Facilities

Hwang

Kim

et al. 2021

Applied Sciences

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In large systems, such as power plants or petrochemical plants, various equipment (e.g., compressors, pumps, turbines, etc.) are typically deployed. Each piece of equipment operates under generally harsh operating conditions, depending on its purpose, and operates with a probability of failure. Therefore, several sensors are attached to monitor the status of each piece of equipment to observe its conditions; however, there are many limitations in monitoring equipment using thresholds such as maximum and minimum values of data. Therefore, this study introduces a technology that can diagnose fault conditions by analyzing several sensor data obtained from plant operation information systems. The equipment for the case study was a main air blower (MAB), an important cooling equipment in the plant process. The driving sensor data were analyzed for approximately three years, measured at the plant. The fault history of the actual process was also analyzed. Due to the large number of sensors installed in the MAB system, a dimension reduction method was applied with the principal component analysis (PCA) method when analyzing collected sensor data. For application to PCA, the collected sensor data were analyzed according to the statistical analysis method and data features were extracted. Then, the features were labeled and classified according to normal and fault operating conditions. The analyzed features were converted with a diagnosis model, by dimensional reduction, applying the PCA method and a classification algorithm. Finally, to validate the diagnosis model, the actual failure signal that occurred in the plant was applied to the suggested method. As a result, the results from diagnosing signs of failure were confirmed even before the failure occurred. This paper explains the case study of fault diagnosis for MAB equipment with the suggested method and its results.

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Section: Detailed Procedures For Fault Diagnosismentioning

confidence: 99%

Section: Detailed Procedures For Fault Diagnosismentioning

confidence: 99%

Section: Dimensionality Reductionmentioning

confidence: 99%

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Application of PCA and Classification for Fault Diagnosis of MAB Installed in Petrochemical Plant Process Facilities

Hwang

Kim

et al. 2021

Applied Sciences

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“…For example, the FMEA process is used in the design and validation of a hydraulic torque converter [5]. Additionally, the diagnosis of the bearing condition using the FMEA process is presented in [6]. Additionally, in [7], Bayesian networks are built from the FMEA process used by Markov decision processes to model uncertainties within unmanned aerial vehicles.…”

Section: Introductionmentioning

confidence: 99%

Improving the Consistency of the Failure Mode Effect Analysis (FMEA) Documents in Semiconductor Manufacturing

Razouk

Kern

2022

Applied Sciences

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Digitalization of causal domain knowledge is crucial. Especially since the inclusion of causal domain knowledge in the data analysis processes helps to avoid biased results. To extract such knowledge, the Failure Mode Effect Analysis (FMEA) documents represent a valuable data source. Originally, FMEA documents were designed to be exclusively produced and interpreted by human domain experts. As a consequence, these documents often suffer from data consistency issues. This paper argues that due to the transitive perception of the causal relations, discordant and merged information cases are likely to occur. Thus, we propose to improve the consistency of FMEA documents as a step towards more efficient use of causal domain knowledge. In contrast to other work, this paper focuses on the consistency of causal relations expressed in the FMEA documents. To this end, based on an explicit scheme of types of inconsistencies derived from the causal perspective, novel methods to enhance the data quality in FMEA documents are presented. Data quality improvement will significantly improve downstream tasks, such as root cause analysis and automatic process control.

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“…Lee et al used image shadow removal and color model binarization, then used the Hough transform to find the switch, and finally identified and compared the switch state [3]. To determine the state of the switch, Kim et al found the switch symbol associated with the circuit breaker switch and performed gray-scale conversion, binarization, smoothing, character extraction, and character pattern matching in the map area of the symbol [4]. Jalil et al proposed a method for evaluating the state of power grid equipment online using multiple measurements.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Identification of Embedded System Equipment State Based on Internet of Things

Xu¹

2022

Mobile Information Systems

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IoT (Internet of things) technology will be more widely used in the manufacturing industry, which will bring new trends to the development of the manufacturing industry. With the continuous advancement of IoT technology in the power field, the integration of IoT and smart grid has become a new topic. In the process of monitoring and troubleshooting the whole EPSE (Embedded Power System Equipment), it is very important to conduct a comprehensive analysis and routine testing on specific equipment and systems, so that the power system can work stably. In this paper, the knowledge sharing and transfer model of EPSE based on IoT is analyzed, and an intelligent identification method of EPSE state based on the GA-C_HMM (Genetic Algorithm-Coupled Hidden Markov Model) algorithm is proposed. After the GA parameters are optimized, the state model library is constructed by fitting each state data of the equipment to C_HMM, and the state of the equipment is determined by calculating the maximum probability value of the signal to be diagnosed. The experimental results show that when the recognition time is 12 hours, the recognition accuracy rate of the existing recognition method is 69.7%, and that of the recognition method in this paper is 98.3%, which shows that the recognition accuracy rate of the recognition method adopted in this study is higher than that of the existing methods and the recognition ability is stronger.

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An Experimental Study on Condition Diagnosis for Thrust Bearings in Oscillating Water Column Type Wave Power Systems

Cited by 6 publications

References 30 publications

Application of PCA and Classification for Fault Diagnosis of MAB Installed in Petrochemical Plant Process Facilities

Application of PCA and Classification for Fault Diagnosis of MAB Installed in Petrochemical Plant Process Facilities

Improving the Consistency of the Failure Mode Effect Analysis (FMEA) Documents in Semiconductor Manufacturing

Intelligent Identification of Embedded System Equipment State Based on Internet of Things

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