5 research outputs found
ΠΠΈΠ±ΡΠΈΠ΄Π½ΡΠ΅ ΡΠ΅ΡΠ΅Π²ΡΠ΅ ΡΡΡΡΠΊΡΡΡΡ ΠΈ ΠΈΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΡΠ»ΠΎΠΆΠ½ΡΡ ΡΠ΅Ρ Π½ΠΈΡΠ΅ΡΠΊΠΈΡ ΡΠΈΡΡΠ΅ΠΌ
An approach to the technical diagnostics of complex technical systems based on the results of telemetry information processing by an external monitoring and diagnostics system using hybrid network structures is proposed. The principle of constructing diagnostic complexes of complex technical systems is considered, which ensures the automation of the technical diagnostics process and is based on the use of models in the form of hybrid network structures for processing telemetric information, including multilayer neural networks and discrete Bayesian networks with stochastic learning. A model of changes in the parameters of complex technical systems technical state based on multilayer neural networks has been developed, which makes it possible to form a probabilistic assessment of attributing the current situation of complex technical system functioning to the set of functions considered situations according to individual telemetry parameters, and multilevel hierarchical model of complex technical systems technical diagnostics based on a discrete Bayesian network with stochastic learning, which allows aggregating the information received from neural network models and recognizing the current situation of complex technical system functioning. In the conditions of functioning emergencies of the complex technical system, according to the results of processing telemetric information, faulty functional units are localized and an explanation of the cause of the emergency is formed. The stages of complex technical systems technical diagnostics implementation using the proposed hybrid network structures in the processing of telemetric information are detailed. An example of using the developed approach to solving problems of spacecraft onboard system technical diagnostics is presented. The advantages of the proposed approach to the technical diagnostics of complex technical systems in comparison with the traditional approach based on analysis of telemetry parameters values belonging to the given tolerances are shown.ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ ΠΊ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠΌΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ»ΠΎΠΆΠ½ΡΡ
ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌ ΠΏΠΎ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΠ΅Π»Π΅ΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π²Π½Π΅ΡΠ½Π΅ΠΉ ΡΠΈΡΡΠ΅ΠΌΠΎΠΉ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ ΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ Π³ΠΈΠ±ΡΠΈΠ΄Π½ΡΡ
ΡΠ΅ΡΠ΅Π²ΡΡ
ΡΡΡΡΠΊΡΡΡ. Π Π°ΡΡΠΌΠΎΡΡΠ΅Π½ ΠΏΡΠΈΠ½ΡΠΈΠΏ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΎΠ² ΡΠ»ΠΎΠΆΠ½ΡΡ
ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌ, ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°ΡΡΠΈΠΉ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·Π°ΡΠΈΡ ΠΏΡΠΎΡΠ΅ΡΡΠ° ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΠΉ Π½Π° ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΈ ΠΏΡΠΈ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ΅ ΡΠ΅Π»Π΅ΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Π² Π²ΠΈΠ΄Π΅ Π³ΠΈΠ±ΡΠΈΠ΄Π½ΡΡ
ΡΠ΅ΡΠ΅Π²ΡΡ
ΡΡΡΡΠΊΡΡΡ, Π²ΠΊΠ»ΡΡΠ°ΡΡΠΈΡ
ΠΌΠ½ΠΎΠ³ΠΎΡΠ»ΠΎΠΉΠ½ΡΠ΅ Π½Π΅ΠΉΡΠΎΠ½Π½ΡΠ΅ ΡΠ΅ΡΠΈ ΠΈ Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΡΠ΅ Π±Π°ΠΉΠ΅ΡΠΎΠ²ΡΠΊΠΈΠ΅ ΡΠ΅ΡΠΈ ΡΠΎ ΡΡΠΎΡ
Π°ΡΡΠΈΡΠ΅ΡΠΊΠΈΠΌ ΠΎΠ±ΡΡΠ΅Π½ΠΈΠ΅ΠΌ. Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½Ρ ΠΌΠΎΠ΄Π΅Π»Ρ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΡΠ»ΠΎΠΆΠ½ΡΡ
ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΌΠ½ΠΎΠ³ΠΎΡΠ»ΠΎΠΉΠ½ΡΡ
Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ
ΡΠ΅ΡΠ΅ΠΉ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠ°Ρ ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°ΡΡ Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠ½ΡΡ ΠΎΡΠ΅Π½ΠΊΡ ΠΎΡΠ½Π΅ΡΠ΅Π½ΠΈΡ ΡΠ΅ΠΊΡΡΠ΅ΠΉ ΡΠΈΡΡΠ°ΡΠΈΠΈ ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ»ΠΎΠΆΠ½ΠΎΠΉ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ ΠΊ ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²Ρ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Π½ΡΡ
ΡΠΈΡΡΠ°ΡΠΈΠΉ ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎ ΠΎΡΠ΄Π΅Π»ΡΠ½ΡΠΌ ΡΠ΅Π»Π΅ΠΌΠ΅ΡΡΠΈΡΡΠ΅ΠΌΡΠΌ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΌ, ΠΈ ΠΌΠ½ΠΎΠ³ΠΎΡΡΠΎΠ²Π½Π΅Π²Π°Ρ ΠΈΠ΅ΡΠ°ΡΡ
ΠΈΡΠ΅ΡΠΊΠ°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ»ΠΎΠΆΠ½ΡΡ
ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΠΎΠΉ Π±Π°ΠΉΠ΅ΡΠΎΠ²ΡΠΊΠΎΠΉ ΡΠ΅ΡΠΈ ΡΠΎ ΡΡΠΎΡ
Π°ΡΡΠΈΡΠ΅ΡΠΊΠΈΠΌ ΠΎΠ±ΡΡΠ΅Π½ΠΈΠ΅ΠΌ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠ°Ρ Π°Π³ΡΠ΅Π³ΠΈΡΠΎΠ²Π°ΡΡ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ ΠΎΡ Π½Π΅ΠΉΡΠΎΡΠ΅ΡΠ΅Π²ΡΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡ ΠΈ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°ΡΡ ΡΠ΅ΠΊΡΡΡΡ ΡΠΈΡΡΠ°ΡΠΈΡ ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ»ΠΎΠΆΠ½ΠΎΠΉ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ. Π ΡΡΠ»ΠΎΠ²ΠΈΡΡ
Π½Π΅ΡΡΠ°ΡΠ½ΡΡ
ΡΠΈΡΡΠ°ΡΠΈΠΉ ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ»ΠΎΠΆΠ½ΠΎΠΉ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ ΠΏΠΎ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΠ΅Π»Π΅ΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π»ΠΎΠΊΠ°Π»ΠΈΠ·ΡΡΡΡΡ Π½Π΅ΠΈΡΠΏΡΠ°Π²Π½ΡΠ΅ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΠ΅ ΡΠ·Π»Ρ ΠΈ ΡΠΎΡΠΌΠΈΡΡΠ΅ΡΡΡ ΠΎΠ±ΡΡΡΠ½Π΅Π½ΠΈΠ΅ ΠΏΡΠΈΡΠΈΠ½Ρ Π²ΠΎΠ·Π½ΠΈΠΊΠ½ΠΎΠ²Π΅Π½ΠΈΡ Π½Π΅ΡΡΠ°ΡΠ½ΠΎΠΉ ΡΠΈΡΡΠ°ΡΠΈΠΈ. ΠΠ΅ΡΠ°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Ρ ΡΡΠ°ΠΏΡ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ»ΠΎΠΆΠ½ΡΡ
ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΡΡ
Π³ΠΈΠ±ΡΠΈΠ΄Π½ΡΡ
ΡΠ΅ΡΠ΅Π²ΡΡ
ΡΡΡΡΠΊΡΡΡ ΠΏΡΠΈ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ΅ ΡΠ΅Π»Π΅ΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ. ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ ΠΏΡΠΈΠΌΠ΅Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΠΎΠ³ΠΎ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π° ΠΊ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π·Π°Π΄Π°Ρ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π±ΠΎΡΡΠΎΠ²ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ ΠΊΠΎΡΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π°ΠΏΠΏΠ°ΡΠ°ΡΠ°. ΠΠΎΠΊΠ°Π·Π°Π½Ρ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²Π° ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΠΎΠ³ΠΎ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π° ΠΊ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠΌΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ»ΠΎΠΆΠ½ΡΡ
ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌ Π² ΡΡΠ°Π²Π½Π΅Π½ΠΈΠΈ Ρ ΡΡΠ°Π΄ΠΈΡΠΈΠΎΠ½Π½ΡΠΌ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠΌ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΠΎΠΌ Π½Π° Π°Π½Π°Π»ΠΈΠ·Π΅ ΠΏΡΠΈΠ½Π°Π΄Π»Π΅ΠΆΠ½ΠΎΡΡΠΈ Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ ΡΠ΅Π»Π΅ΠΌΠ΅ΡΡΠΈΡΡΠ΅ΠΌΡΡ
ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² Π·Π°Π΄Π°Π½Π½ΡΠΌ Π΄ΠΎΠΏΡΡΠΊΠ°ΠΌ
ΠΠΈΠ±ΡΠΈΠ΄Π½ΡΠ΅ ΡΠ΅ΡΠ΅Π²ΡΠ΅ ΡΡΡΡΠΊΡΡΡΡ ΠΈ ΠΈΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΡΠ»ΠΎΠΆΠ½ΡΡ ΡΠ΅Ρ Π½ΠΈΡΠ΅ΡΠΊΠΈΡ ΡΠΈΡΡΠ΅ΠΌ
ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ ΠΊ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠΌΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ»ΠΎΠΆΠ½ΡΡ
ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌ ΠΏΠΎ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΠ΅Π»Π΅ΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π²Π½Π΅ΡΠ½Π΅ΠΉ ΡΠΈΡΡΠ΅ΠΌΠΎΠΉ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ ΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ Π³ΠΈΠ±ΡΠΈΠ΄Π½ΡΡ
ΡΠ΅ΡΠ΅Π²ΡΡ
ΡΡΡΡΠΊΡΡΡ. Π Π°ΡΡΠΌΠΎΡΡΠ΅Π½ ΠΏΡΠΈΠ½ΡΠΈΠΏ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΎΠ² ΡΠ»ΠΎΠΆΠ½ΡΡ
ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌ, ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°ΡΡΠΈΠΉ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·Π°ΡΠΈΡ ΠΏΡΠΎΡΠ΅ΡΡΠ° ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΠΉ Π½Π° ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΈ ΠΏΡΠΈ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ΅ ΡΠ΅Π»Π΅ΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Π² Π²ΠΈΠ΄Π΅ Π³ΠΈΠ±ΡΠΈΠ΄Π½ΡΡ
ΡΠ΅ΡΠ΅Π²ΡΡ
ΡΡΡΡΠΊΡΡΡ, Π²ΠΊΠ»ΡΡΠ°ΡΡΠΈΡ
ΠΌΠ½ΠΎΠ³ΠΎΡΠ»ΠΎΠΉΠ½ΡΠ΅ Π½Π΅ΠΉΡΠΎΠ½Π½ΡΠ΅ ΡΠ΅ΡΠΈ ΠΈ Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΡΠ΅ Π±Π°ΠΉΠ΅ΡΠΎΠ²ΡΠΊΠΈΠ΅ ΡΠ΅ΡΠΈ ΡΠΎ ΡΡΠΎΡ
Π°ΡΡΠΈΡΠ΅ΡΠΊΠΈΠΌ ΠΎΠ±ΡΡΠ΅Π½ΠΈΠ΅ΠΌ. Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½Ρ ΠΌΠΎΠ΄Π΅Π»Ρ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΡΠ»ΠΎΠΆΠ½ΡΡ
ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΌΠ½ΠΎΠ³ΠΎΡΠ»ΠΎΠΉΠ½ΡΡ
Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ
ΡΠ΅ΡΠ΅ΠΉ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠ°Ρ ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°ΡΡ Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠ½ΡΡ ΠΎΡΠ΅Π½ΠΊΡ ΠΎΡΠ½Π΅ΡΠ΅Π½ΠΈΡ ΡΠ΅ΠΊΡΡΠ΅ΠΉ ΡΠΈΡΡΠ°ΡΠΈΠΈ ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ»ΠΎΠΆΠ½ΠΎΠΉ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ ΠΊ ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²Ρ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Π½ΡΡ
ΡΠΈΡΡΠ°ΡΠΈΠΉ ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎ ΠΎΡΠ΄Π΅Π»ΡΠ½ΡΠΌ ΡΠ΅Π»Π΅ΠΌΠ΅ΡΡΠΈΡΡΠ΅ΠΌΡΠΌ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΌ, ΠΈ ΠΌΠ½ΠΎΠ³ΠΎΡΡΠΎΠ²Π½Π΅Π²Π°Ρ ΠΈΠ΅ΡΠ°ΡΡ
ΠΈΡΠ΅ΡΠΊΠ°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ»ΠΎΠΆΠ½ΡΡ
ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΠΎΠΉ Π±Π°ΠΉΠ΅ΡΠΎΠ²ΡΠΊΠΎΠΉ ΡΠ΅ΡΠΈ ΡΠΎ ΡΡΠΎΡ
Π°ΡΡΠΈΡΠ΅ΡΠΊΠΈΠΌ ΠΎΠ±ΡΡΠ΅Π½ΠΈΠ΅ΠΌ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠ°Ρ Π°Π³ΡΠ΅Π³ΠΈΡΠΎΠ²Π°ΡΡ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ ΠΎΡ Π½Π΅ΠΉΡΠΎΡΠ΅ΡΠ΅Π²ΡΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡ ΠΈ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°ΡΡ ΡΠ΅ΠΊΡΡΡΡ ΡΠΈΡΡΠ°ΡΠΈΡ ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ»ΠΎΠΆΠ½ΠΎΠΉ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ. Π ΡΡΠ»ΠΎΠ²ΠΈΡΡ
Π½Π΅ΡΡΠ°ΡΠ½ΡΡ
ΡΠΈΡΡΠ°ΡΠΈΠΉ ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ»ΠΎΠΆΠ½ΠΎΠΉ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ ΠΏΠΎ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΠ΅Π»Π΅ΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π»ΠΎΠΊΠ°Π»ΠΈΠ·ΡΡΡΡΡ Π½Π΅ΠΈΡΠΏΡΠ°Π²Π½ΡΠ΅ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΠ΅ ΡΠ·Π»Ρ ΠΈ ΡΠΎΡΠΌΠΈΡΡΠ΅ΡΡΡ ΠΎΠ±ΡΡΡΠ½Π΅Π½ΠΈΠ΅ ΠΏΡΠΈΡΠΈΠ½Ρ Π²ΠΎΠ·Π½ΠΈΠΊΠ½ΠΎΠ²Π΅Π½ΠΈΡ Π½Π΅ΡΡΠ°ΡΠ½ΠΎΠΉ ΡΠΈΡΡΠ°ΡΠΈΠΈ. ΠΠ΅ΡΠ°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Ρ ΡΡΠ°ΠΏΡ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ»ΠΎΠΆΠ½ΡΡ
ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΡΡ
Π³ΠΈΠ±ΡΠΈΠ΄Π½ΡΡ
ΡΠ΅ΡΠ΅Π²ΡΡ
ΡΡΡΡΠΊΡΡΡ ΠΏΡΠΈ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ΅ ΡΠ΅Π»Π΅ΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ. ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ ΠΏΡΠΈΠΌΠ΅Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΠΎΠ³ΠΎ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π° ΠΊ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π·Π°Π΄Π°Ρ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π±ΠΎΡΡΠΎΠ²ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ ΠΊΠΎΡΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π°ΠΏΠΏΠ°ΡΠ°ΡΠ°. ΠΠΎΠΊΠ°Π·Π°Π½Ρ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²Π° ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΠΎΠ³ΠΎ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π° ΠΊ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠΌΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ»ΠΎΠΆΠ½ΡΡ
ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌ Π² ΡΡΠ°Π²Π½Π΅Π½ΠΈΠΈ Ρ ΡΡΠ°Π΄ΠΈΡΠΈΠΎΠ½Π½ΡΠΌ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠΌ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΠΎΠΌ Π½Π° Π°Π½Π°Π»ΠΈΠ·Π΅ ΠΏΡΠΈΠ½Π°Π΄Π»Π΅ΠΆΠ½ΠΎΡΡΠΈ Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ ΡΠ΅Π»Π΅ΠΌΠ΅ΡΡΠΈΡΡΠ΅ΠΌΡΡ
ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² Π·Π°Π΄Π°Π½Π½ΡΠΌ Π΄ΠΎΠΏΡΡΠΊΠ°ΠΌ
Estimating the probability distributions of radioactive concrete in the building stock using Bayesian networks
The undesirable legacy of radioactive concrete (blue concrete) in post-war dwellings contributes to increased indoor radon levels and health threats to occupants. Despite continuous decontamination efforts, blue concrete still remains in the Swedish building stock due to low traceability as the consequence of lacking systematic documentation in technical descriptions and drawings and resource-demanding large-scaled radiation screening.The paper aims to explore the predictive inference potential of learning Bayesian networks for evaluating the presence probability of blue concrete. By integrating blue concrete records from indoor radon measurements, pre-demolition audit inventories, and building registers, it is possible to estimate buildings with high probabilities of containing blue concrete and encode the dependent relationships between variables. The findings show that blue concrete is estimated to be present in more than 30% of existing buildings, more than the current expertassumptions of 18β20%. The probability of detecting blue concrete depends on the distance to historical blue concrete manufacturing plants, building class, and construction year, but it is independent of floor area and basements. Multifamily houses and buildings built between 1960 and 1968 or nearby manufacturing plants are more likely to contain blue concrete. Despite heuristic, the data-driven approach offers an overview of the extent and the probability distribution of blue concrete-prone buildings in the regional building stock. The paper contributes to method development for pattern identification for hazardous building materials, i.e., blue concrete, and the trained models can be used for risk-based inspection planning before renovation and selective demolition
Modelling the causation of accidents: human performance separated system and human performance included system
Jedes Jahr ereignen sich weltweit Millionen von ArbeitsunfΓ€llen, die zahlreiche Opfer fordern und enorme wirtschaftliche Verluste zur Folge haben. Vorangegangene Studien aus dem Feld der RisikoeinschΓ€tzung zeigten, dass es wichtig ist die Wahrscheinlichkeit von Faktoren, welche zum Auftreten von UnfΓ€llen beitragen, zu quantifizieren. Mehrere Methoden, wie z. B. die Technik zur Vorhersage der menschlichen Fehlerrate (Technique for Human Error Rate Prediction, THERP), wurden dafΓΌr vorgeschlagen, potenzielle Risikofaktoren zu bewerten und die Systemsicherheit zu verbessern. Diese Methoden haben jedoch einige EinschrΓ€nkungen, wie z.B. ihre geringe Generalisierbarkeit, die Behandlung von Unfallursachen und menschlichem Einfluss als zwei voneinander getrennte Forschungsthemen, die Notwendigkeit ausgiebiger DatensΓ€tze, oder die ausschlieΓliche AbhΓ€ngigkeit von Expertenwissen.
Um diese EinschrΓ€nkungen zu ΓΌberwinden, 1) klassifiziert diese Dissertation die Systeme in zwei Kategorien. Zum einen in von menschlichem Einfluss separierte Systeme (Human Performance Separated System, HPSS) und zum anderen in Systeme mit menschlichem Einfluss (Human Performance Included System, HPIS); 2) entwickelt ein auf Bayesβschen Netzwerken (BN) basierendes UnfallkausalitΓ€tsmodell, das auf beide Arten von Systemen angewendet werden kann, um den Einfluss menschlicher Wahrnehmung in HPSS und den Einfluss menschlichen Versagens in HPIS zu untersuchen; 3) untersucht zwei Methoden zur Analyse menschlichen Versagens. Die erste Methode geht von einer kognitiven Wahrnehmung aus und die zweite behandelt das menschliche Versagen als essenziellen Teil des Systems. 4) schlΓ€gt eine innovative Taxonomie namens Contributors Taxonomy for construction Occupational Accidents (CTCOA) fΓΌr HPIS vor, die nicht nur auf die UnfallkausalitΓ€t
abzielt, sondern auch zur RΓΌckverfolgung menschlichen Versagens im Bauwesen verwendet werden kann. 5) erstellt BN-Beispielmodelle aus unterschiedlichen Industriesektoren. Dazu zΓ€hlen GasturbinenausfΓ€lle als typisches Beispiel fΓΌr HPSS-Maschinenversagen, das Multi-Attribute Technological Accidents Dataset (MATA-D) fΓΌr einfaches HPIS-Systemversagen und das Contributors to Construction Occupational Accidents Dataset (CCOAD) fΓΌr komplexes HPIS-Systemversagen. Diese drei BN-Modelle zeigen, wie die von uns vorgeschlagene Methode in Bezug auf spezifische Probleme aus verschiedenen Industriesektoren angepasst und angewendet werden kann.
Unsere Analyse zeigt die Effizienz der Kombination von Expertenwissen und mathematischer UnabhΓ€ngigkeitsanalyse bei der Identifizierung der wichtigsten AbhΓ€ngigkeitsbeziehungen innerhalb der BN-Struktur. Vor der Parameteridentifizierung auf Basis von Expertenwissen sollten die Auswirkungen der menschlichen Wahrnehmung auf die Modellparameter gemessen werden. Die vorgeschlagene Methodik basierend auf der Kombination der menschlichen ZuverlΓ€ssigkeitsanalyse mit statistischen Analysen kann zur Untersuchung menschlichen Versagens eingesetzt werden.Millions of work-related accidents occur each year around the world, leading to a large number of deaths, injuries, and a huge economic cost. Previous studies on risk assessment have revealed that it is important to calculate the probabilities of factors that can contribute to the occurrence of accidents. Several methods, such as the Technique for Human Error Rate Prediction (THERP), have been proposed to evaluate potential risk factors and to improve system safety. However, these methods have some limitations, such as their low generalizability, treating accident causation and human factor as two separate research topics, requiring intensive data, or relying solely on expert judgement.
To address these limitations, this dissertation 1) classifies systems into two types, Human Performance Separated System (HPSS) and Human Performance Included System (HPIS), depending on whether the system involves human performance; 2) develops accident causal models based on Bayesian Network (BN) that can be applied to both types of systems while examining the influence of human perception in HPSS and human errors in HPIS; 3) examines two methods for the analysis of human errors with the first method based on the cognitive view and the other method treating human errors as an essential part of the system; 4) proposes an innovative taxonomy as an example for HPIS, known as the Contributors Taxonomy for Construction Occupational Accidents (CTCOA), which not only targeting accident causation, but can also be used for tracking human error in construction; 5) builds example BN models in the different industrial sectors, including gas turbine failures as a typical example of HPSS machine failures, Multi-Attribute Technological Accidents Dataset (MATA-D) as simple HPIS failures, and Contributors to Construction Occupational Accidents Dataset (CCOAD) as complex HPIS failures. These three types of BN models demonstrate how our proposed methodology can be adapted to specific questions and how it can be applied in various industrial sectors.
Our analysis demonstrates that it is efficient to combine expert judgement with mathematical independence analysis to identify the main dependency links for the BN structure in all models. The influence of human perception on model parameters should be measured before these parameters being identified based on expert judgement. Our proposed methodology can be used to study human errors by combining traditional human reliability analysis with statistical analysis
Learning Bayesian network parameters via minimax algorithm
Parameter learning is an important aspect of learning in Bayesian networks. Although the maximum likelihood algorithm is often effective, it suffers from overfitting when there is insufficient data. To address this, prior distributions of model parameters are often imposed. When training a Bayesian network, the parameters of the network are optimized to fit the data. However, imposing prior distributions can reduce the fitness between parameters and data. Therefore, a trade-off is needed between fitting and overfitting. In this study, a new algorithm, named MiniMax Fitness (MMF) is developed to address this problem. The method includes three main steps. First, the maximum a posterior estimation that combines data and prior distribution is derived. Then, the hyper-parameters of the prior distribution are optimized to minimize the fitness between posterior estimation and data. Finally, the order of posterior estimation is checked and adjusted to match the order of the statistical counts from the data. In addition, we introduce an improved constrained maximum entropy method, named Prior Free Constrained Maximum Entropy (PF-CME), to facilitate parameter learning when domain knowledge is provided. Experiments show that the proposed methods outperforms most of existing parameter learning methods