IoT Predictive Maintenance
One of the biggest challenges in large-scale industrial machine deployments today is to keep track of the functioning of the machines, and to minimize the downtime. There is a huge benefit in being able to predict accurately the failure of a particular machine.
At Persistent, we understand this challenge and have build solutions that:
- Integrates IoT sensors data from diverse sources.
- Profiles the sensory data, to detect anomalies and raise alerts.
- Predicts future breakdowns and minimizes operative costs.
- Promotes ideal usage of machines by detecting incorrect settings.
Customer Success Snapshot
A specialist in design, manufacture and support of electrical control panels, displays, remote controls, and wiring systems for all engine based machines.
- Accurately predicting a machine’s future breakdowns was difficult that resulted into high operation costs, increased downtime, and delayed repairs. This called for integration of machines to send real time sensory data.
- Each machine had a different working profile which had to be learnt based on historical data before anomalies could be identified. Using a manual which gave approximate running parameters was not sufficient to get the working profile.
- Data from sensors was often missing which made learning the machine profile difficult.
- Developed an end-to-end solution integrating data from sensors deployed on mechanical machines in the field and analyzing the data at run-time to detect anomalies.
- Historical data (cleaned up using ETL processes) was used to develop a Machine Learning system to profile the engines and learn their default behavior in the presence of other seasonal factors.
- Following conditions and parameters were tracked for an alert-based dashboard system:
- Determining fuel economy degradation to alert if more fuel would be used for similar workloads.
- A probabilistic model to learn the ideal oil pressure value at different engine speeds, to raise alerts for system diversions from these values for a prolonged period of time.
- To alert when system observed an abnormal amount of time taken for the oil pressure to stabilize at engine start-up time.
- Reduced number of breakdowns, raised alerts when the system behaved abnormally, and reduced downtime due to timely preventive maintenance of machines.
- Reduced overall cost of operation by optimizing operational parameters and reducing human oversight.
- Helped to study the wear and tear in machines whenever the new profiles diverted from the ideal working profiles.
- Inefficient usage by operators was detected and avoided which resulted in considerable amount of fuel savings, and helped prevent wear and tear.
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