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.
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.
| № | Муаллифнинг исми | Лавозими | Ташкилот номи |
|---|---|---|---|
| 1 | Ruziev U.A. | researcher | TSTU |
| № | Ҳавола номи |
|---|---|
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