Quote from
sachinm on 7 February 2024, 3:28 am
Automated Asset Inspections with Machine Learning
Traditional risk assessment, and management approaches, have been very labour-intensive. One only has to think of the Risk Practitioner having to "walk-the-floor" and get assessment views from Subject Matter experts, and Project Managers, on the latest view on inherent/residual risk exposure and response strategies.
Not surprisingly, there has been a greater emphasis placed on predictive maintenance strategies, and anomaly detection to allow earlier detection and intervention of risks. These methods use past real-life project data by obtaining analytics visibility and gaining insights through artificial intelligence (AI) and machine learning (ML). These ML powered automated data analysis tools allow general inspection that enables predictive maintenance. Companies can then configure QC validation process and data alerts to monitor sensor data in case of exceedance of safety thresholds. Afterwards, companies can use correlation alerts for defining alert thresholds dynamically in the form of linear and quadratic relationships between two sensor metrics.
We can simplify anomaly detection models to automatically flags critical incidents. Machine Learning (ML) powered automated data analysis tool for general inspection that enables predictive maintenance by providing consistent and reproducible results.
Automatic identification of rare items, events, or observations in data
Anomaly Detection is the identification of rare items, events, or observations in data that differ significantly from the expectation. This can be used for several scenarios like asset monitoring, maintenance, and prognostic surveillance in industries such as utility, aviation, and manufacturing.
The Anomaly Detection Service will create customized Machine Learning models, by taking the data uploaded by users, using MSET algorithm, which is a multivariate anomaly detection algorithm to train the model, and deploying the model into the cloud environment to be ready for detection.
MSET is a nonlinear, nonparametric anomaly detection machine learning technique that calibrates the expected behavior of a system based on historical data from the normal operational sequence of monitored signals. It incorporates the learned behavior of a system into a persistent model that represents the normal estimated behavior.
Users can then send new data to the detection endpoints to get the detected anomaly results. This then allows them to raise data alerts to monitor sensor data in case of exceedance of safety thresholds using the following methods:
TIME-SERIES DATA ANALYSIS
- Utilise time series charts with trending, annotation, and analytic capabilities.
- Develop X/Y and scatter plots.
DIGITAL TWIN VISUALISATION
- Utilise 4D navigation to display historical versions of the models and IoT.
- Configure data alerts to monitor sensor data in case of exceedance of safety thresholds.
- Display map layers from ArcGIS, WMS and WMTS servers on 3D terrain.
- Visualise 3D point cloud captured with laser scanners
Automated Asset Inspections with Machine Learning
Traditional risk assessment, and management approaches, have been very labour-intensive. One only has to think of the Risk Practitioner having to "walk-the-floor" and get assessment views from Subject Matter experts, and Project Managers, on the latest view on inherent/residual risk exposure and response strategies.
Not surprisingly, there has been a greater emphasis placed on predictive maintenance strategies, and anomaly detection to allow earlier detection and intervention of risks. These methods use past real-life project data by obtaining analytics visibility and gaining insights through artificial intelligence (AI) and machine learning (ML). These ML powered automated data analysis tools allow general inspection that enables predictive maintenance. Companies can then configure QC validation process and data alerts to monitor sensor data in case of exceedance of safety thresholds. Afterwards, companies can use correlation alerts for defining alert thresholds dynamically in the form of linear and quadratic relationships between two sensor metrics.
We can simplify anomaly detection models to automatically flags critical incidents. Machine Learning (ML) powered automated data analysis tool for general inspection that enables predictive maintenance by providing consistent and reproducible results.
Automatic identification of rare items, events, or observations in data
Anomaly Detection is the identification of rare items, events, or observations in data that differ significantly from the expectation. This can be used for several scenarios like asset monitoring, maintenance, and prognostic surveillance in industries such as utility, aviation, and manufacturing.
The Anomaly Detection Service will create customized Machine Learning models, by taking the data uploaded by users, using MSET algorithm, which is a multivariate anomaly detection algorithm to train the model, and deploying the model into the cloud environment to be ready for detection.
MSET is a nonlinear, nonparametric anomaly detection machine learning technique that calibrates the expected behavior of a system based on historical data from the normal operational sequence of monitored signals. It incorporates the learned behavior of a system into a persistent model that represents the normal estimated behavior.
Users can then send new data to the detection endpoints to get the detected anomaly results. This then allows them to raise data alerts to monitor sensor data in case of exceedance of safety thresholds using the following methods:
TIME-SERIES DATA ANALYSIS
- Utilise time series charts with trending, annotation, and analytic capabilities.
- Develop X/Y and scatter plots.
DIGITAL TWIN VISUALISATION
- Utilise 4D navigation to display historical versions of the models and IoT.
- Configure data alerts to monitor sensor data in case of exceedance of safety thresholds.
- Display map layers from ArcGIS, WMS and WMTS servers on 3D terrain.
- Visualise 3D point cloud captured with laser scanners
