Introduction
As mentioned in the previous article “Fusion of Plant Engineering Ontology ISO15926 and AI Technology” plant engineering is a complex technology involving many elements and requiring a vast amount of knowledge data, so ontology technology is actively applied. In this article, I would like to discuss the application of ontology technology to plant engineering from an operational perspective.
Ontology-based knowledge platform to support equipment health in plant operations
The following steps could be taken to provide equipment health support in plant operations using an ontology-based knowledge platform.
Construction of an ontology model
First, a detailed ontology model of the equipment and systems in the plant is built. An ontology is a framework that represents information from different data sources in a unified format and helps to understand relationships. Although public ontologies such as ISO 15926 exist, domain-specific knowledge requires a separate ontology model to be built and extended. The ontology model is expected to include information about equipment types, characteristics, maintenance history, failure modes, components, etc.
The steps and key points for building an ontology model are described below. 1.
1. definition of target domain:
First, clearly define the plant operational domain to be covered. Determine which equipment, systems, and processes to focus on and gather basic information about the subject.
2. cooperation of domain experts:
To build an ontology model, domain experts must be consulted.Cooperation of domain experts is essential for building the ontology model. It is necessary to work with people who have expertise in equipment and plant operations to understand the key concepts and relationships.
3. terminology collection and organization:
Collect and organize terms and concepts relevant to plant operations. This includes equipment types, characteristics, functions, components, failure modes, maintenance procedures, etc. These terms will later be represented as concepts in the ontology model.
4. hierarchical structure design:
Ontology models are typically expressed in a hierarchical structure, where the hierarchy is designed from high-level concepts to detailed concepts. This could be, for example, a hierarchy starting with the entire plant, then equipment types, specific equipment, components, maintenance processes, etc.
5. relevance definition:
The ontology model should also define the relationships between concepts. This allows for the representation of relationships and dependencies among equipment and processes. For example, if certain failure modes are related to certain maintenance procedures, those relationships should be incorporated into the model.
6. attribute definition:
Define appropriate attributes for each concept. These attributes help to represent the characteristics and information of the concept. This could include, for example, for equipment, attributes such as date of manufacture, maintenance history, conditions of use, etc.
7. implementation of the ontology model:
Implement the ontology model as an actual database or knowledge graph. This should be done in such a way that concepts, attributes, relationships, etc. are properly mapped.
8. data integration and updating:
It is important for an ontology-based knowledge platform to integrate with real-world data and update the information. As new data and information become available, the ontology model will be updated appropriately.
9. testing and evaluation:
The built ontology model will be tested and evaluated to ensure that it is functioning properly. This will improve the quality of the model while correcting errors and making improvements.
Automation using machine learning techniques is an important component of these steps. See also “Automatic Knowledge Graph Generation and Various Implementation Examples” and “Various Uses and Implementation Examples of Knowledge Graphs” for more information.
Data Integration
The next step is to collect information from various data sources within the plant and integrate it into an ontology-based platform. This includes sensor data, maintenance logs, manufacturing data, design information, etc. The integrated data will be mapped to be consistent with the ontology. The details of data integration are described below. 1.
1. identification of data sources:
First, the different data sources relevant to plant operations are identified. This includes sensor data, maintenance logs, manufacturing data, design information, historical failure data, product specifications, etc. Identify all data sources and identify which types of information are needed.
2. unify data formats:
It will be common for data from different data sources to be provided in different formats. The first step in data integration will be to convert these data into a unified format. This includes data formatting, cleansing, and standardization.
3. data mapping and integration into an ontology:
Based on the ontology model, data will be mapped to the appropriate ontology concepts. Data fields and attributes will be linked to correspond to ontology concepts, e.g., temperature information from sensor data will be mapped to the “temperature” concept in the ontology model.
4. building a data integration platform:
A data integration platform will be built to integrate data and to store and manage data mapped according to the ontology model. This platform will support data acquisition, storage, query processing, and access control.
5. integration of real-time data feeds:
Real-time data is critical to plant operations. By integrating sensor and control data in real-time and incorporating it into an ontology-based platform, problems can be detected and responded to immediately.
6. data security and privacy:
The data integration process requires appropriate measures to ensure data security and privacy. Security mechanisms such as access control, encryption, and audit trails should be implemented to protect data confidentiality.
7. data updating and maintenance:
The data integration platform should be designed to allow for rapid updates when data changes, as well as regular data cleansing and formatting to maintain data quality.
8. data quality control:
Data quality control is an important aspect of maintaining the integrity of the integrated data. This will allow for monitoring the accuracy, consistency, and completeness of the data and addressing quality issues as they arise.
For data and ontology mapping, machine learning approaches such as those described in “Similarity in Machine Learning” are important. For real-time data handling, see “Machine Learning and System Architecture for Data Streams (Time-Series Data)” and for security techniques, see “Encryption and Security Techniques and Data Compression Techniques.
Data Analysis and Monitoring
Once data integration is complete, the next step is to analyze the integrated data based on the ontology and monitor the health of the equipment. This will enable the system to automatically warn of any anomalies that are detected and suggest necessary actions, thereby optimizing the prevention of failures and the timing of repairs. The importance of data analysis and monitoring and the steps involved are described below.
1. data collection and integration:
A prerequisite for data analysis and monitoring is the collection and integration of information from different data sources into an ontology-based platform. This includes sensor data, maintenance logs, manufacturing data, and design information. Conversion and formatting of data into a unified format will be required.
2. data preprocessing:
Since the collected data often contains noise and missing values, preprocessing is performed to improve data quality, including removing noise, completing missing values, and detecting and processing outliers.
3. selection of the data analysis algorithm:
The appropriate data analysis algorithm is selected based on the type of data and the goals of the project. This may involve a variety of approaches, including machine learning, statistical analysis, and pattern recognition. In particular, anomaly detection algorithms are an important technique in health support.
4. model training and application:
Train a model using selected data analysis algorithms to develop metrics for evaluating equipment health. The model will be used to monitor the condition of equipment in the plant and detect anomalies.
5. real-time data processing:
Real-time data is critical to plant operations, and it is important to implement mechanisms to collect real-time data from data streams and apply data analysis models to immediately detect anomalies.
6. alerts and notifications:
Data analysis and monitoring systems can send alerts and notifications to operations personnel and managers when anomalies are detected. This allows problems to be identified and addressed as early as possible. 7.
7. data visualization:
To effectively communicate the results of data analysis.Data visualization is useful for effectively communicating the results of data analysis. Visualization allows the use of dashboards and graphs to visualize equipment status and trends, providing intuitive information to operations personnel.
8. model evaluation and improvement:
The data analysis model will be evaluated on a regular basis, and adjustments and improvements will be made to improve performance. The model will be updated as new data and information become available.
Various methods of data analysis are summarized in “Machine Learning Techniques. See also “Machine Learning Techniques. For anomaly detection in particular, see “Anomaly and Change Detection Techniques” and “Time Series Data Analysis. For real-time data handling, see “Machine Learning and System Architecture for Data Streams (Time-Series Data)” for data visualization, see “User Interface and Data Visualization Techniques” and for model evaluation, see “Statistical Hypothesis Testing and Machine Learning Techniques.
Predictive Maintenance
Once data can be analyzed and monitored, the next step is the application of predictive maintenance methods to support plant equipment health. This means using data to predict future failures and optimize maintenance schedules, utilizing ontologies to gain insight into what equipment requires what maintenance and when it should be performed.
This predictive maintenance is an approach to predict equipment failures and faults and to perform planned maintenance activities, which contributes to minimizing production downtime and reducing maintenance costs. The methods and processes for predictive maintenance are described below.
1 Data Collection and Integration:
The data underlying predictive maintenance will be collected and integrated into an ontology-based platform. This data includes sensor data, maintenance logs, historical failure data, manufacturing data, and environmental data. Data will be converted and formatted into a unified format.
2. ontology utilization:
An ontology-based knowledge platform will provide a unified representation of equipment and plant knowledge. This ontology can be leveraged to map data to related concepts, for example, sensor data can be associated with attributes related to the health of a particular piece of equipment or instrument.
3. feature engineering:
Extract or generate appropriate features (characteristics) from data to train predictive models. Using ontologies, it is possible to associate features with concepts related to equipment condition and performance. 4.
4. train the predictive model:
Train a predictive model using the data. These models are used to predict equipment failures or faults and use machine learning algorithms or statistical models to learn patterns from historical data and predict future failures. 5.
5. real-time data monitoring:
Real-time data is critical in plant operations, and ontology-based knowledge platforms are used to monitor sensor and control data in real time and apply predictive models to detect anomalies.
6. alerts and notifications:
When the predictive model detects anomalies, alerts and notifications are sent to operations and maintenance teams. This allows problems to be identified early and planned maintenance activities to be implemented.
7. maintenance schedule optimization:
Optimize maintenance schedules based on predictive maintenance. Utilize the information provided by the predictive model to determine which equipment requires maintenance at what point in time. 8.
8. continuous model improvement:
Predictive models are regularly evaluated and improved by incorporating new data and information. Adjustments will be made as appropriate to improve the accuracy of the model.
IOT technology is a key component of data collection; see “Sensor Data & IOT Technology.” For real-time data monitoring, see “Machine Learning and System Architecture for Data Streams (Time-Series Data)” and for the application of ontology technology to such data, see “Working Process Quantification in Factory Using Wearable Sensor Device and Ontology-based Stream Data Processing” for the application of ontology technology to them. See also “Automata and State Transitions/Petri Net and Automatic Planning” for schedule optimization.
Documentation and Troubleshooting
Once predictive maintenance begins to take place, the next step is documentation to keep track of those instances. An ontology-based knowledge platform supports the troubleshooting process by efficiently documenting information about the equipment. This will make it easier for operations personnel to quickly find appropriate countermeasures when problems arise. These elements are described below.
1. documentation:
Equipment Information Documentation:
An ontology-based knowledge platform will be used to document information about all equipment in the plant. This includes equipment type, date of manufacture, manufacturer information, product specifications, blueprints, and operating manuals. The ontology will organize this information in a unified format to understand its relevance.
Maintenance Process Documentation:
Information about maintenance activities should also be documented. This includes regular maintenance schedules, maintenance procedures, records of preventive maintenance activities, replacement parts information, etc. This information will be integrated into an ontology to support the maintenance process.
Troubleshooting Guide:
The ontology-based knowledge platform provides guides for troubleshooting. When an abnormal equipment condition is detected, relevant documentation is retrieved to provide troubleshooting procedures and countermeasures. This allows operational personnel to effectively resolve problems.
2. troubleshooting:
Anomaly Detection and Alerts:
The ontology-based knowledge platform monitors real-time data to detect equipment anomalies. When an anomaly is detected, the system sends an alert to the appropriate operations personnel so that the problem can be identified early.
Utilizing Troubleshooting Guides:
The ontology-based knowledge platform will provide troubleshooting guides to help operations personnel diagnose and resolve problems. Ontology-based documentation and troubleshooting guides help operations personnel effectively resolve problems.
Root Cause Analysis:
The ontology-based knowledge platform supports root cause analysis of anomalies. It identifies the causes of anomalies and suggests countermeasures to resolve the root causes, thereby providing information to prevent future problems.
Historical Data Analysis:
Analyze historical trouble and failure data to identify patterns and trends. This provides information to predict future problems and take appropriate countermeasures.
An important part of the various types of documentation is the unification of terms and concepts used. The use of an ontology makes it possible to integrate them efficiently (see Ref). It is also easy to link the various failure analysis concepts as described in “On Failure Risk Analysis and Ontology (FMEA, HAZID)“.
Collaboration and Alerts
As a further step, the system could send alerts to operational personnel when equipment conditions are abnormal, prompting them to take necessary actions, and could also be linked to other business systems to improve productivity. Details on integration and alerting are described below.
Integration:
1 Integration with Data Sources:
Plant operations involve many data sources, including sensor data, control systems, maintenance logs, and design information, which feed data into an ontology-based knowledge platform. Data federation should occur in real time and should support data collection, transformation, integration, and stream processing.
2. federation with ontology models:
Ontology-based knowledge platforms have ontology models that represent the equipment, processes, and relationships within a plant. This can be leveraged to map information from data sources to relevant ontology concepts to make sense of the data.
3. linkage with other systems:
Plant operations may require integration with other systems. Integration with various systems, such as enterprise resource planning (ERP) systems, maintenance management systems, SCADA systems, etc., may be achieved to ensure consistency and efficiency of information.
Alerting:
1 Anomaly detection and alerting:
Data analysis and anomaly detection algorithms could be used to detect equipment anomalies in real-time, and when an anomaly is detected, an alert could be generated to notify the appropriate operations or maintenance team, or alerts could be prioritized according to their importance.
2. alert notification methods:
Alerts can be notified in a variety of ways. Email, SMS, mobile app notifications, alert messages on the dashboard, etc., provide a way for operations personnel to take immediate action, and notification methods could be customized to meet operational requirements.
3. alert escalation:
Some alerts should be escalated if appropriate action is not taken, and since critical issues require a high level of response, an alert escalation process should be defined and managed.
Alert logging and analysis: Alerts generated should be logged.
Alerts generated are logged and can be used for later analysis and evaluation. Analyzing the history of alerts will enable prediction of future problems and system improvements.
Collaboration and alerting through an ontology-based knowledge platform will improve the ability to provide real-time information and address issues in support of equipment health in plant operations, which will result in many benefits, including increased productivity, reduced risk, and more efficient maintenance.
Continuous Improvement
The final step is continuous improvement. Supporting equipment health in plant operations is an ongoing process that requires continuous work to collect data, improve systems, and integrate new information to improve operational efficiency. The following describes methods and processes focused on continuous improvement.
1. improving data quality:
The first step in continuous improvement is to improve data quality. Ensure improved data quality from data sources to maintain data accuracy, completeness, and consistency. Quickly detect and take action to address poor data quality and inconsistencies.
2. model tuning and training:
Regularly evaluate and tune the predictive models and anomaly detection algorithms used. Retrain models as new data becomes available and update the ontology to improve performance and incorporate new knowledge and insights. 3.
3. enhance the troubleshooting guide:
Troubleshooting guides are an important resource for operations personnel to resolve issues. Improve and expand the guides to help resolve issues quickly and use lessons learned from past problems to create new guidelines.
4. integrate new data sources:
As new sensor data and data sources become available, integrate these data sources into the ontology-based platform. This will provide more comprehensive information and insights to improve plant operational health support.
5. user feedback collection:
Proactively collect feedback from operations personnel and maintenance teams and use it to improve the system. Adjust functionality in response to user requests and needs to improve ease of use.
6. ongoing training and education:
Provide training and education to the plant operations team on new tools and approaches. Update knowledge and enhance skills on the latest best practices and data analysis techniques.
7. benchmarking and pursuit of industry best practices:
Provide training and education to plant operations teams on new tools and approaches. Research and implement best practices from other plants and industries to improve the efficiency of plant operations and the quality of health support.
8. goal setting and measurement:
Set goals and measure progress regularly for continuous improvement. Evaluate achievement of goals and adjust strategies as necessary.
The continuous improvement process enables the effective evolution of equipment health support in plant operations to provide benefits such as optimal use of resources, increased productivity, risk reduction, and cost savings. Adapting to changes in the plant operations environment and adopting the latest technologies and best practices are keys to success.
Reference Books and Reference Information
For reference information on ontology, see “Ontology Technology” and for reference information on knowledge information processing in general, see “Knowledge Information Processing Technology“.
Reference book is “Ontology Modeling in Physical Asset Management”
“Knowledge Graphs and Big Data Processing“
“The Knowledge Graph Cookbook“
“Domain-Specific Knowledge Graph Construction“
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