Predictive Maintenance uses embedded sensors to potentially monitor hundreds of parameters without human intervention and detect departure from normal operational parameters continuously without needing highly specialized tools or experts. A machine learning based approach to predictive maintenance can learn continuously and generalize from new data – potentially identifying breakdowns that have never been observed before. This not only reduces overall cost and frequency of maintenance, but also reduces reliance on specialized tools and expertise required for traditional predictive maintenance.

Our holistic approach starts with an assessment of the current maintenance and downtime performance of your organization. This is followed by:

• Working with the maintenance department to ensure the traditional prerequisites (work-order backlog, bad-actor list, etc.) are in place
• Identification of equipment that might be the best candidates for the predictive maintenance program
• Review historical maintenance records and conduct focus workshops with SME’s to help identify typical failure modes
• Evaluation of the data-gathering infrastructure (sensors, data collection, storage and query, etc.) for selected equipment and identifying gaps
• Machine Learning models are trained on “normal” operations and setup to identify any deviations from or anomalies in normal operations
• Machine learning models are self-learning and can react to the changing manufacturing environment
• Alerts and notifications are set in place along with standard operating (corrective) procedures
• The system is finally incorporated into the client business processes along with end-user training
• A robust change management initiative runs throughout the entire duration of the implementation to help realize the financial benefits of the predictive maintenance program

• Improve overall life of asset
• Decrease equiment and plant downtime
• Reduce Maintenance, Repair & Operations (MRO) spend
• Reduce overall maintenance costs
• Reduce safety incidents and liabilities
• Decrease total capital spend