Welcome to Prescriptive Maintenance on Cloud

IBM® Prescriptive Maintenance on Cloud looks for patterns in how an equipment asset is used and the environment in which it is operating. It then correlates this information with any known failures in the equipment. These correlations are used to evaluate new data about the equipment asset, resulting in predictive scores that indicate the relative health of the equipment and the likelihood of future failures.

The ability to determine when maintenance should be performed on equipment assets leads to the following business benefits:

You can estimate and extend the life of your assets.
You can increase the return on your assets.
You can optimize your maintenance, inventory, and resource schedules.

Prescriptive Maintenance on Cloud goes beyond both preventive and regularly scheduled maintenance to ensure asset performance, thereby enabling maximized value at every step of a process. Using Prescriptive Maintenance on Cloud, you can perform the following tasks:

Predict the failure of an instrumented asset so that you can prevent costly unexpected downtime.
Make adjustments to maintenance schedules and tasks to reduce repair costs and minimize downtime.
Determine the most effective maintenance cycles.
Identify the root cause of asset failure faster so that you can take corrective actions.

Instrumented assets generate data such as device ID, timestamp, temperature, and status code.

Examples of instrumented assets are manufacturing equipment, mining equipment, drilling equipment, farming equipment, security equipment, cars, trucks, trains, helicopters, engines, cranes, oil platforms, and wind turbines.

Data from instrumented assets and data from other sources such as maintenance records, maintenance logs, inspection reports, repair invoices and warranty claims can be collected and used in models that predict when an asset is likely to fail.

Prescriptive Maintenance on Cloud helps an organization optimize its maintenance program by developing a set of recommendations to carry out when specific changes in asset health are identified. These recommendations can be based upon analysis of historical maintenance records, best practices and procedures provided by subject matter experts, original equipment manufacturer recommendations, as well as analysis of correlations that indicate potential problems or pending asset failure. The goal of developing these optimized decisions or recommendations is to further improve maintenance practices. When an anomaly in asset performance is identified specific recommendations can be made to maintenance personnel in order to affect the most efficient remedy of the problem. In the case of a manufacturing organization which provides service or warranty support for products that are used by hundreds or thousands of customers, the ability to monitor asset performance in the field and proactively initiate a service call, in comparison to waiting for the client to initiate a repair request or warranty claim, can significantly transform the way the manufacturer provides service. With a better understanding of asset usage and performance in the field manufacturer may wish to modify its warranty program based upon asset usage or warranty costs. Additionally greater insight into asset usage and performance can help the manufacturer to optimize the parts inventory and locations so as to reduce the volume of inventory and identify locations to facilitate proactive customer service.

For example, an automobile assembly plant is a system that combines thousands of pieces of equipment with interlocking pieces. It is critical that such a system is able to work efficiently and produce safe, high quality products. Prescriptive Maintenance on Cloud looks for patterns in the usage and environmental information for equipment that correlate with failures that take place. These patterns are used to create predictive models to score incoming new data in order to predict the likelihood of failure. Scores that are generated from this information give an indication of the health of the piece of equipment. In addition, key performance indicators (KPIs) are collected, which are used for reporting. KPIs help to identify assets that do not conform to normal patterns of behavior. The plant employees can use dashboards and reports to monitor and track the lifecycle of each piece of equipment.