Bayesian Uncertainty Inferencing for Fault Diagnosis of Intelligent Instruments in IoT Systems
Intelligent instruments are common components in industrial machinery, and fault diagnosis in IoT systems requires the handling of real-time sensor data and expert knowledge. IoT sensors cannot collect data for the diagnosis of all fault types in a specific instrument, and long-distance data transfe...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-04-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/9/5380 |
| _version_ | 1797603071345295360 |
|---|---|
| author | Qing Liu Chengcheng Wang Qiang Wang |
| author_facet | Qing Liu Chengcheng Wang Qiang Wang |
| author_sort | Qing Liu |
| collection | DOAJ |
| description | Intelligent instruments are common components in industrial machinery, and fault diagnosis in IoT systems requires the handling of real-time sensor data and expert knowledge. IoT sensors cannot collect data for the diagnosis of all fault types in a specific instrument, and long-distance data transfer introduces additional uncertainties. However, because industrial equipment has complex fault causes and performances, it is typically difficult or expensive to obtain exact fault probabilities. Therefore, in this study, we proposed an innovative failure detection and diagnosis model for intelligent instruments in an IoT system using a Bayesian network, with a focus on handling uncertainties in expert knowledge and IoT monitoring information. The model addresses the challenge of complex fault causes and performances in industrial equipment, which make the obtainment of exact fault probabilities difficult or expensive. The trapezoidal intuitionistic fuzzy number (TrIFN)-based entropy method was applied in order to aggregate expert knowledge to generate priority probability, and the Leaky-OR gate was used to calculate CPT. The effectiveness of the proposed strategy was demonstrated through its application to an intelligent pressure transmitter (IPT) using the GeNIe software. |
| first_indexed | 2024-03-11T04:24:19Z |
| format | Article |
| id | doaj.art-7a18e145d93a4aedb1df9abae2204be6 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T04:24:19Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-7a18e145d93a4aedb1df9abae2204be62023-11-17T22:33:12ZengMDPI AGApplied Sciences2076-34172023-04-01139538010.3390/app13095380Bayesian Uncertainty Inferencing for Fault Diagnosis of Intelligent Instruments in IoT SystemsQing Liu0Chengcheng Wang1Qiang Wang2College of Quality and Safety Engineering, China Jiliang University, Hangzhou 310018, ChinaStandard and Test Center, Instrumentation Technology and Economy Institute, Beijing 100055, ChinaCollege of Quality and Safety Engineering, China Jiliang University, Hangzhou 310018, ChinaIntelligent instruments are common components in industrial machinery, and fault diagnosis in IoT systems requires the handling of real-time sensor data and expert knowledge. IoT sensors cannot collect data for the diagnosis of all fault types in a specific instrument, and long-distance data transfer introduces additional uncertainties. However, because industrial equipment has complex fault causes and performances, it is typically difficult or expensive to obtain exact fault probabilities. Therefore, in this study, we proposed an innovative failure detection and diagnosis model for intelligent instruments in an IoT system using a Bayesian network, with a focus on handling uncertainties in expert knowledge and IoT monitoring information. The model addresses the challenge of complex fault causes and performances in industrial equipment, which make the obtainment of exact fault probabilities difficult or expensive. The trapezoidal intuitionistic fuzzy number (TrIFN)-based entropy method was applied in order to aggregate expert knowledge to generate priority probability, and the Leaky-OR gate was used to calculate CPT. The effectiveness of the proposed strategy was demonstrated through its application to an intelligent pressure transmitter (IPT) using the GeNIe software.https://www.mdpi.com/2076-3417/13/9/5380intelligent instrumentIoTBayesian networkTrIFNLeaky-OR gate |
| spellingShingle | Qing Liu Chengcheng Wang Qiang Wang Bayesian Uncertainty Inferencing for Fault Diagnosis of Intelligent Instruments in IoT Systems Applied Sciences intelligent instrument IoT Bayesian network TrIFN Leaky-OR gate |
| title | Bayesian Uncertainty Inferencing for Fault Diagnosis of Intelligent Instruments in IoT Systems |
| title_full | Bayesian Uncertainty Inferencing for Fault Diagnosis of Intelligent Instruments in IoT Systems |
| title_fullStr | Bayesian Uncertainty Inferencing for Fault Diagnosis of Intelligent Instruments in IoT Systems |
| title_full_unstemmed | Bayesian Uncertainty Inferencing for Fault Diagnosis of Intelligent Instruments in IoT Systems |
| title_short | Bayesian Uncertainty Inferencing for Fault Diagnosis of Intelligent Instruments in IoT Systems |
| title_sort | bayesian uncertainty inferencing for fault diagnosis of intelligent instruments in iot systems |
| topic | intelligent instrument IoT Bayesian network TrIFN Leaky-OR gate |
| url | https://www.mdpi.com/2076-3417/13/9/5380 |
| work_keys_str_mv | AT qingliu bayesianuncertaintyinferencingforfaultdiagnosisofintelligentinstrumentsiniotsystems AT chengchengwang bayesianuncertaintyinferencingforfaultdiagnosisofintelligentinstrumentsiniotsystems AT qiangwang bayesianuncertaintyinferencingforfaultdiagnosisofintelligentinstrumentsiniotsystems |