Effective Predictive Maintenance process can be instrumental in reducing maintenance overheads by evaluating the asset health and alerting on the downtimes/maintenance thereby reducing the asset operating costs by as much as 60%.
Machine learning and AI-enabled predictive and prescriptive insights to reduce breakdowns, improve operational reliability, optimize component replacement process, improve product quality & safety, and reduce maintenance costs. Gain real-time and historical insight into asset health, diagnose root-causes of failures, detect anomalies and get early warnings of conditions that are out of tolerance.
Aximind’s solution application is built to predict and alert the stakeholders before the downtime and machine breakdown. The machine failure prediction helps the relevant stakeholders to estimate and identify which machine is going to fail irrespective of the telemetry information.
Through Aximind’s tool, the production asset data is fetched from the sensors to a central repository using industrial communication protocols and gateways. Data from all the systems including any ERP or MES are integrated into the central data repository to provide context to the production asset data. Then, Aximind’s Predictive Maintenance tool identifies key drivers and trends leading up to the “bubble pop” event and recommends optimal system settings to minimize occurrence of the pop. These bubble pop events happen when the machines spark a breakage in their regular working process leading to a malfunction or failure in the machines.
Developing a plan that is custom-built for your business.