AI can analyse large amounts of data to find accurate content for a given topic, but what about checking the consistency of data which we have gathered from content writers and users of data gathering systems.
The accuracy and consistency of collected data is crucial for efficient and safe operations in the mining industry. Inconsistent data can mean accidents, inefficiencies, and safety hazards. Here are some of the ways in which we can use AI for consistency checking of data.
- Machine learning algorithms?can analyse large amounts of sensor data,?production logs,?reports, and other data sources to identify inconsistencies, anomalies, and errors.?These anomalies could include unexpected variations in equipment data,?used forestall accidents and outages. It could be variations from planned production figures,?or even anomalies in such as geological data, which may well alert management to change their plans at certain times.
- If we use predictive models then these can be used to anticipate potential data inconsistencies based on historical patterns of data gathered, together with any changes in operating parameters.?This can all be used to ‘head-off’ potential outages or bring forward planned maintenance activities.
- Natural Language Processing?techniques can be used to compare data from different sources,?e.g., geological reports,?operational logs,?financial records, and a variety of other sources.?This ‘compare and contrast’ approach can reveal anomalies in gathered data or in plant and machinery data.
- If rules can be established for some collected data, then any anomalies which arise as exceptional values, can be used to flag anomalous data items which may be significant in efficiency, safety, or operational contexts.
Integration with other systems
- AI solutions can be integrated with other existing systems, such as reporting or process control systems,?to collect and analyse data seamlessly.?This can avoid the need for manual data extraction and manipulation,?and so reduce errors and streamline the process of passage od data.
- Improved accuracy and reliability of data being brought together by AI rather than relying on manual juggling of data will significantly reduce error, and bias. It will inevitably lead to more accurate and reliable data being amassed and presented.
- Identifying and addressing data inconsistencies quickly can prevent costly downtime,?optimize resource allocation,?and improve overall operational efficiency. Significant strides can be made in this area as some of the insights will not have even been measured before.
- Early detection of anomalies can aid in preventing accidents and safety hazards related to inaccurate or malfunctioning equipment, operational errors, or possible situations which have been identified ahead of time.
- Better decision-making is facilitated when everyone has access to standardised data in a timely manner, thus potentially improving performance and reducing outages and incidents.
- Ensuring high quality data is a fundamental requirement for AI systems, which can be a challenge in establishing, both in quality and completeness.
- For situations where we are integrating AI with in-house systems there may be significant technical expertise required to perform the integrations. This will be made more complex by the need for expertise in both AI and in the existing software technology.
- Implementing AI over the range of situations we have described above requires a degree of openness, clarity, and also a mechanism for answering questions about the basis for what has been put in place.
