Optimize equipment reliability by listening to your data

By | 3 minute read | September 1, 2017

Really big data

The world is more connected than ever before. Using Internet of Things (IoT) technologies, manufacturers are collecting large amounts of data from their machines but do not know how to optimize it. It’s as if their machines are speaking on mute. They have so much to say but we simply cannot hear them.

To help deal with this massive overload of data, IBM released the new IBM Predictive Maintenance and Optimization (IBM PMO) solution. It takes large amounts of data from machines and analyzes it for patterns that can help predict equipment failure. It provides a detailed view into equipment performance. This information is used to optimize maintenance efforts, ensuring the right equipment is always available.

Do you really know the life span of your assets?

You may have a rough estimation of how long your assets and equipment will last but how confident are you in that estimate? It could be much more or much less – and you won’t know until it’s too late. Here are just a few benefits that IBM PMO helps enable:

  • Models calculate asset health scores and predict life spans
  • Real-time interactive dashboards monitor assets and processes
  • Detect asset failures and quality issues earlier
  • Explore asset performance data to learn the cause of failure
  • Provide optimized maintenance recommendations to operations
  • Customize solutions for your specific maintenance use cases

Focus on reliability

Equipment is of no use when it is broken. It can cause major problems if equipment fails in the middle of production. Building on the strengths of previous predictive maintenance solutions, IBM PMO focuses on the needs of the reliability engineer to identify and manage risks that could result in failure or a halt in operations.  A reliability engineer can build a model to determine remaining equipment life and improve maintenance strategy. The analysis helps them identify the state of current production equipment and identifies impending failure.

Optimize performance of critical equipment

For the most critical equipment, where unplanned downtime has a major impact on production and repair costs are high, it is possible to build custom predictive models. These models prepare key data and use the expertise of a data scientist to perform additional analysis. This effort can avoid huge financial loss, and minimize negative impacts by ensuring maintenance is scheduled at the most optimum times.

Pre-built predictive models for similar equipment

While custom models are ideal for some organizations, for others it will not be necessary. Organizations that have critical equipment of similar type or class (e.g., generators, motors, pumps, robots)  can use the pre-built app. This is most effective when unplanned downtime impacts production and cumulative maintenance costs are significant. Use of these standard, pre-built models enables you to monitor and analyze a variety of equipment and their current maintenance schedules. This differs from custom-built models which are specific to certain equipment.

The pre-built application enables reliability engineers to obtain both high-level and detailed reports of performance and maintenance history.  It supports analysis and reporting on all equipment, classes of equipment, or filters for properties common to a set of equipment. This flexible reporting makes it faster to analyze and understand current maintenance practices and prioritize future needs.

Understand what leads to failure

The ability to collect data including failures, maintenance history, time stamps, metrics, and events is another valuable capability of this new solution. IBM PMO aligns various pieces of  data to a fixed interval so that it can examine the relationship between multiple variables collected at different points in time (see Figure 1).  It then offers recommendations to improve maintenance strategy for individual equipment or equipment classes. It also recommends actions to take based on predictive scoring and identification of factors that positively and negatively influence equipment health. This provides a detailed comparison of historical factors affecting equipment performance.

optimize equipment maintenance

Figure 1. Comparison of data points at different time intervals.

Quickly assess maintenance needs and optimize performance

Simply put, IBM PMO allows reliability personnel to gain an understanding of all factors that affect equipment performance. This information provides a full picture for assessing past, present, and future equipment performance and needs.

Learn more about how you can improve maintenance practices

Read more about what IBM PMO can do in this two-page brief.

Ready to better understand the key benefits to your operations and speak to an expert? Visit the marketplace today.

Interested in offerings available on the cloud? Learn more about additional  IBM IoT for manufacturing solutions, including offerings specific to quality, product development, and enterprise asset management.