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On failure risk analysis and ontology (FEMA, HAZID)
Failure risk analysis is a method of assessing the risk of failure of a system, such as machinery or equipment, and predicting the likelihood and impact of a failure. Failure risk analysis is an important task for improving safety and reliability and is used in various industrial fields.
An ontology is a systematization of knowledge about a particular domain, which defines concepts, attributes, and relationships in that domain. This ontology is useful in failure risk analysis to define terms and concepts such as risk factors and evaluation indicators in a unified manner. For example, in machinery failure risk analysis, risk factors include machine parts, functions, operating conditions, etc. By using the ontology, these terms and concepts can be defined consistently and have a common understanding in different analyses.
The ontology can also be used to structure and manage information necessary for failure risk analysis. For example, data on different machines and equipment can be managed in an integrated manner by classifying data such as failure history and repair history based on an ontology.
Furthermore, the ontology can be used to visualize the results of failure risk analysis in an easy-to-understand manner. This allows, for example, the creation of a graph for each risk factor, which enables an intuitive understanding of the likelihood and impact of failure.
This section describes this failure risk analysis and ontology based on “Ontology Modeling in Physical Asset Management,” Chapter 3: FMEA, HAZID, and Ontology.
FMEA stands for Failure Mode and Effect Analysis, and is a systematic method of analyzing potential failures for the purpose of preventing failure problems.
FTA (Fault Tree Analysis) is a similar failure analysis method, but FTA is a top-down method in which the undesirable events of a product are first assumed, and the possible paths to failure and accidents are described in a tree structure with the probability of occurrence (see the figure below).
機械振興協会のFTA解説図より
FMEA is a bottom-up analysis method that describes the failure events that lead to failure, rather than the failure itself (function). Specifically, after organizing the system information (structure, functions, components, etc.) in preparation, the following FMEA sheet is prepared, which lists the failure modes, their effects, and the assumed failure modes.
機械振興協会FMEAワークシートの作成方法より
HAZID is an abbreviation for Hazard Identification Study, which is a method for safety assessment of plants and systems to identify potential risks (hazards) and evaluate their magnitude. The identification of hazards is done by using the What-if method or its improved version, the Structured What-if Technique (SWIFT). In this method, a structured worksheet is used and questions such as “What-if”, “How could”, and “It is possible” are asked. It is used for brainstorming based on questions such as “what-if,” “how could,” and “it is possible,” in order to anticipate various problems in advance.
This book introduces FMEA and HAZID, followed by specific examples of combining them with ontologies. The table of contents is shown below.
By combining the knowledge organized by these ontologies with IOT technologies represented by stream data handling, machine learning technologies such as sparse modeling, deep learning, and time series data analysis, and various inference technologies, it will be possible to build an autonomous risk management system.
3.1 What Is FMEA 3.1.1 Why FMEA 3.1.2 FMEA:What Is the Problem 3.1.3 How to Do the FMEA 3.1.4 Prioritizing 3.1.5 How Can We Use the FMEA Results 3.1.6 Failure Modes 3.1.7 FMEA for the System 3.2 What Is HAZID 3.2.1 HAZID as an Alternative Approach 3.2.2 HAZID Based on Function 3.2.3 HAZID Based on Components 3.3 The System's Environment 3.3.1 Generic Fault Tree 3.3.2 Environmental Hazard Lists 3.3.3 Combining FMEAs 3.3.4 Case-Consequence Diagrams 3.4 What Is an Ontology 3.4.1 On Ontologies 3.4.2 How to Build an Ontology 3.4.3 Why Do We Need Ontologies in FMEA 3.4.4 The Component Ontology 3.4.5 System Ontologies 3.4.6 Ontologies and Failure Propagation 3.4.7 A Simple Control Loop Example 3.4.8 The Steam Boiler Ontology Example 3.5 What Is Ontology Good and Where Are Humans Better 3.5.1 The Need for a Tool 3.5.2 Fitts' List References
Combining Ontology Technology with Failure Risk Analysis
The following examples can be considered when combining ontology technology with failure risk analysis.
- Systematization and unification of knowledge: Ontology technology can be used to systematically organize and unify knowledge and information related to failure risk analysis. Ontologies can model concepts and relationships and clearly define failure-related terms and concepts to enable common understanding among different people and organizations.
- Knowledge reuse and sharing: Ontologies facilitate reuse and sharing of knowledge related to failure risk analysis. Ontologies are a formal knowledge representation method, which makes it easier to integrate with other systems and tools, and furthermore, sharing ontologies enables knowledge sharing and collaboration among different projects and organizations.
- Automation and efficiency: Ontology technologies can be combined to automate and streamline the process of failure risk analysis. The knowledge and rules embedded in ontologies can be used to automatically perform failure risk assessment and prediction, and ontology-based systems and tools can be developed to simplify tasks and generate results quickly.
- Risk visualization and interpretation: Ontology technology facilitates visualization and understanding of failure risk. Ontologies can be represented as graphs or diagrams, allowing for an intuitive understanding of risk factors and relationships. In addition, ontologies can be used to infer and interpret risks to discover anomalous patterns and hidden associations.
- Leveraging Domain Knowledge: Combining ontology techniques enhances the use of domain knowledge in failure risk analysis. Since ontologies systematically represent knowledge about a specific domain, they can incorporate expert knowledge and experience. This enables more accurate failure risk assessment and prediction.
As described above, combining ontology technology with failure risk analysis will result in more effective knowledge management and risk management.
Combining AI Technology with Failure Analysis Technology
The following examples can be considered by combining AI technology with failure analysis technology.
- Data analysis and pattern recognition: AI technology can be used to efficiently analyze large amounts of failure data and sensor data to recognize abnormal patterns and failure factors. This would utilize machine learning algorithms and pattern recognition methods to extract patterns and correlations from the data to predict and identify failures.
- Predictive Maintenance and Anomaly Detection: AI technology can be applied to detect failures and predictive maintenance in real time. This allows sensor and monitoring data to be input into AI models to detect abnormal behavior and performance degradation, enabling early detection of signs of failure and countermeasures to be taken.
- Risk assessment and prioritization: AI techniques can be combined to efficiently assess and prioritize failure risks. Machine learning models and statistical methods can be used to estimate risk factors and impact and prioritize them in order of risk, thereby enabling optimal allocation of resources and budgets.
- Fail-safe design and optimization: AI techniques can be used to perform fail-safe design and optimization of systems and products. This can be done using AI models and simulations to predict abnormal conditions and failure scenarios, identify and improve design weaknesses, and plan for optimal maintenance schedules and preventive maintenance activities.
- Knowledge management and decision support: By combining AI technology, the results of failure analysis and knowledge can be stored as a knowledge base and used for decision support, using AI models and inference engines to suggest appropriate actions and preventive measures based on failure analysis results and past responses. This will be possible.
As described above, combining failure analysis technology with AI technology will enable more efficient failure detection, prediction, risk assessment, fail-safe design, and decision support. This will contribute to minimizing failure risks and improving system reliability.
Combining AI and ontology technologies with failure analysis technology
The following are possible examples of combining ontology and AI technologies with failure risk analysis.
- Knowledge integration and sharing: Ontologies and AI can be used to automatically integrate, manage, and share knowledge and information related to failure risk analysis. This will enable a more comprehensive understanding of failure risk by integrating knowledge from different data sources and domains into the ontology.
- Knowledge Automation and Retrieval: The use of ontology-driven AI techniques facilitates automation and retrieval of knowledge relevant to failure analysis. By building a knowledge base based on ontologies and applying AI models, users can automatically leverage the knowledge and rules necessary for failure analysis, and users can ask questions and queries in natural language, and AI systems can search for knowledge in ontologies and provide appropriate answers and information.
- Data Analysis and Pattern Recognition: By using AI technology to analyze large amounts of failure and sensor data, perform pattern recognition, and combine ontology knowledge with AI models, it will be possible to extract failure characteristics and factors and detect abnormal patterns. This enables early failure prediction and risk assessment.
- Risk assessment and optimization: Combining AI and ontology technologies enhances risk assessment and optimization: AI models can be used to estimate risk factors and impact, prioritize them in order of risk, and combine ontology-based knowledge with AI models to optimize maintenance schedules and preventive maintenance activities can be planned.
- Knowledge management and decision support: By combining ontology and AI technologies, the results of failure analysis and knowledge can be accumulated as a knowledge base and used for decision support. This would integrate ontology-based knowledge base and AI models to propose appropriate actions and preventive measures based on failure analysis results.
- Automation and efficiency: AI technologies can be combined to automate and streamline the process of failure risk analysis. A system integrating ontologies and AI models will automatically collect data, analyze, and generate results to support rapid risk assessment and decision making.
- Real-time monitoring and feedback: Failure risk analysis systems utilizing AI technology can monitor data in real time to detect abnormalities and signs of failure. By combining ontology-based knowledge with AI algorithms, the system can constantly monitor the status of the system in operation and suggest appropriate countermeasures and maintenance.
The combination of ontology and AI technology in these failure risk analyses will enable more advanced analysis and effective risk management.
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