Belangrijkste concepten
This study proposes a hybrid framework that integrates deep learning and knowledge-based techniques to detect and extract causal relationships from nuclear power plant event reports.
Samenvatting
The study focuses on extracting causal relationships from nuclear licensee event reports (LERs) submitted to the U.S. Nuclear Regulatory Commission. The key highlights and insights are:
Data Preprocessing:
The raw data consisted of 92 LERs related to motor-driven pump failures.
The text was preprocessed to remove unwanted characters, normalize whitespaces, and generate individual sentences.
A dataset of 20,129 records was created, with each record containing a text sample (up to 3 sentences), a label indicating whether it contains a causal relationship, and the identified cause and effect.
Causality Classification:
A deep learning model was developed to classify sentences as either containing a causal relationship or not.
The model achieved high average accuracies of 99.7% and 99.1% on the training and test sets, respectively.
The model demonstrated strong performance in detecting causal sentences, with recall ratios of 100% and 91% on the training and test sets.
Cause-Effect Extraction:
A knowledge-based approach was used to extract the specific cause and effect segments from the sentences identified as containing causal relationships.
The algorithm identified and matched the cause and effect segments with a high degree of accuracy, correctly extracting 181 out of 252 cause-effect pairs.
The main challenges were in handling embedded causal sentences, which require further improvement.
Conclusion:
The study presents a comprehensive framework that combines deep learning and knowledge-based techniques to effectively detect and extract causal relationships from nuclear power plant event reports.
The authors plan to expand the text corpus, label more causal samples, and develop advanced language models for improved causality extraction in the future.
Statistieken
The DB-50 supply breaker to Auxiliary Feedwater Pump 21 did not close due to inertial latch binding.
The foam ring had deteriorated causing a piece of the foam to tear loose and be drawn into the suction piping of the "A" MDAFW pump.
The bearing degradation was due to insufficient tolerance in the motor shaft endplay, as set during refurbishment.
A subsequent cause evaluation determined that a similar 2B MDEFW pump trip on February 1, 2021 was also caused by the intermittent poor electrical connection due to the loose shorting screws.
Citaten
"The foam ring had deteriorated causing a piece of the foam to tear loose and be drawn into the suction piping of the 'A' MDAFW pump."
"The bearing degradation was due to insufficient tolerance in the motor shaft endplay, as set during refurbishment."
"A subsequent cause evaluation determined that a similar 2B MDEFW pump trip on February 1, 2021 was also caused by the intermittent poor electrical connection due to the loose shorting screws."