METHODS FOR DIAGNOSING ERRORS OF INTELLIGENT MEASUREMENT DEVICES

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13 сентябр 2023

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METHODS FOR DIAGNOSING ERRORS OF INTELLIGENT MEASUREMENT DEVICES

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Technical science and innovation

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MAQOLA ANNOTATSIYASI

Protecting technical systems from loss of performance and failure, as well as the need to comply with safety and efficiency requirements, poses more and more important problems in diagnosing errors in measuring instruments. When designing smart sensors, one should take into account the possibility of self-diagnostics of the system to prevent accidental failures of measuring instruments. In the paper, the most used methods for detecting faults are investigated, on the basis of which a classification of fault detection methods is given. The possibilities of using these methods in intelligent measuring instruments are disclosed. As part of the work, the diagnostic functions of autonomous sensors are considered, where methods of structural redundancy are used, characterized by the fact that the sensitive elements of the sensor are located directly in the technological process. The developed measurement systems with several redundant sensors have a sufficiently low uncertainty of the measured values even in complex technological processes. Frequently occurring hardware and software failures in measuring instruments are identified, on the basis of which the rules between errors and symptoms are determined. The function of self-diagnostics of the system based on neural networks is proposed. A block diagram of a method for detecting errors based on a process model is given. The paper considers the use of algorithms for determining faults in measuring instruments based on neural networks. Based on the artificial intelligence algorithm, a method for diagnosing measuring instruments has been improved, which is able to detect faults in integrated circuits of instrumentation and primary converter with acceptable accuracy.

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Teglar

# intelligent sensors# self-diagnostic# adaptive thresholds# measurement instrument failures# error detection methods# instrument reliability# neural network training

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ROI:

https://eroi.uz/11.0008/A0923-0025

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