Table of Contents
Predicting Maintenance and Downtime Events
Rad Anandakumar is a leading expert in data analytics, machine learning, and AI, with a career spanning more than 2 decades. As an AI + Data Technology Strategist, he has led digital transformations and AI-driven business solutions, working with cutting-edge technologies like Generative AI, IoT, and predictive analytics.
Summary
Mining and Resources Operators are able to replace manual checklists, closed-loop technologies and human senses with telemetry in the environment to collect millions of data points about temperature, sounds, humidity, vibrations, pressure, fluid levels, speed, location, weights, air quality, barcodes, weather and more to actively forecast potential downtime events as well as pinpoint optimal service intervals.
Real-time alerts and warnings will advise operators of the upcoming need to replace parts and automated intervention can even be used to prevent expensive breakdowns and/or damage to equipment thus enabling the process of predicting maintenance and downtime.
Data can be processed in real-time on IoT Edge devices in zero-trust or low connectivity environments, with insights sent into the cloud for aggregation and modelling improvement as needed.
Data Collected
Sensors in the Environment
- Temperature, sounds, humidity, vibrations, pressure, fluid levels, speed, location, weights, air quality, barcodes, weather, etc.
- Unstructured image data, audio data and video data.
- Equipment and circuit settings, run hours, resource consumption, network settings, environmental monitoring.
- Flow rates, levels, weights, feeder speeds, pressure, etc.
- Scheduled and unscheduled downtime event history.
- Log data from circuits, vehicles, equipment, etc.
Historian Data, Log Data and External Data
Key Technologies and Processes
Collect and Centralise the Data
- Accelerators to ingest IoT data, history and telemetry.
- Join data based on date and time to correlate events.
Transform the Data into Useful Insights and Predictors
- Machine learning and analytics to identify indicators for failures and service requirements.
- Live stream data to enable analytics and monitoring in real-time for operators as well as KPIs and dashboards.
- Identify and send alerts and warnings to mobile devices or monitoring screens for intervention.
Outcomes
Predictive, rather than Preventative Maintenance results in replacing parts and performing services less often. Parts are changed once worn or performing poorly rather than arbitrarily replacing them at predetermined intervals. This leads to lower long-term costs and greater production efficiency. Overall Equipment Effectiveness (OEE) key performance indicators can be used to measure productivity and identify potential process losses.
Similarly, predicting downtime leads to increased production, increased revenue and more efficient operations, at a lower cost.
Rad Anandakumar is a leading expert in data analytics, machine learning, and AI, with a career spanning more than 2 decades. As an AI + Data Technology Strategist, he has led digital transformations and AI-driven business solutions, working with cutting-edge technologies like Generative AI, IoT, and predictive analytics.
