Predictive maintenance is designed to determine the condition of equipment and predict when maintenance should be performed, resulting in convenient scheduling of corrective maintenance and cost savings over preventive maintenance as tasks are performed only when warranted. To be done right, the necessary premise is to have the right information at the right time. Below are six solutions that help manufacturers achieve exactly that.
Altizon’s solution is Datonis Manufacturing Intelligence, an enterprise business application that integrates information from plant-floor systems to provide insights.
Datonis MI defined KPIs for measuring Productivity, improving Quality, enabling product Genealogy and Part Traceability and performing Predictive Maintenance of manufacturing assets.
IBM’s PMO (Predictive Maintenance and Optimization) enables asset-intensive organizations to apply machine learning and analytics to improve maintenance strategies and minimize the cost of maintenance management.
IBM PMO focuses on the needs of the reliability engineer to identify and manage equipment reliability risks,as well as make industrial manufacturing, production processes, and products more efficient and dependable.
Adlink has created Vortex Edge PMQ, a fully integrated hardware, data connectivity & predictive analytics solution.
Mnubo enables a product-centric approach to servicing equipment; by using artificial intelligence models to predict machine downtime, schedule proactive maintenance and predict time to action for equipment requiring replenishment/replacement, manufacturers can
Identify impending faults early and reduce costly emergency repairs
Predict when a component will fail and proactively schedule maintenance plans
Identify failure of an asset with longer lead time to improve reliability and performance
Drive mission-critical decisions such as which spare parts to replace
Plant personnel can use n-Join’s predictive maintenance technology to help detect, early on, which equipment repairs and spare parts replacements will be necessary and when, enabling them to pinpoint optimal moments to perform maintenance and minimize loss of productivity. The result is more efficient, profitable, and environmentally sustainable operations for the factory.
Augury’s diagnostic technology is built around the idea that principle every mechanical system can be characterized by the sound that it makes; they then use vibration and ultrasonic sensors to measure these sounds and understand what the machines are saying. The resulting data is then used to detect the slightest changes and warn of developing malfunctions in real-time.
According to Augury, predictive maintenance can offer the following beneficial results:
25–30% average reduction in maintenance costs
70–75% average reduction in downtime
35–45% elimination of breakdowns
20–25% average reduction in energy consumption