1. What predictive maintenance is
Predictive maintenance is a technique to predict the future failure point of a machine component, so that the component can be replaced, based on a plan, just before it fails. Thus, equipment downtime is minimized and the component lifetime is maximized. Predictive maintenance is one of the areas that benefit most from machine learning algorithms with predictive capability.
2. How to start and implement predictive maintenance
Step 1: Gain visibility of your data
Step 2: Understand the gaps and remediate with added sensing
Step 3: Analyze and understand key trends toward faults
Step 4: Leverage trends to predict faults before they happen and optimize maintenance strategy
Step 5: Learn and adjust operations
Step 6: Establishing a reliability culture
3. Applying IoT to facilitate predictive maintenance
The advent of Internet of Things is changing the way we do predictive maintenance. You can now have real-time monitoring devices at a low cost that send data to an algorithm on a continuous basis. It can detect whether there’s something going wrong with a machine or use machine learning to make a prediction.
You can now take advantage of having real-time sensors installed on equipment. They will provide real-time data that you can put into predictive models to help you determine when something is about to fail or what the remaining useful life is for that equipment. You can then schedule maintenance based on that data.
4. How Sentenance can help you achieve your predictive maintenance
Sentenance is the only IoT and FM platform. We offer you the platform that can not only monitor data from the sensors you installed on site but also synchronize such data with the work management feature. This will automate the workflow from sensor alert to work order management. Importantly, we have a Machine Learning add-on module to help you determine a failure periodand mode of your machines/assets so that you can take actions before they fail. Here, we provide an all-in-one predictive maintenance solution to you.