Your data might not be your data.

Previous Predikto bloggers have emphasized the centrality of data in the successful adoption of predictive maintenance initiatives.

Leveraging the full range of equipment operation, environmental and maintenance data, operators can better align the “right” resources to the appropriate mission requirements and/or environmental conditions, and more easily transition to effective implementation of condition-based maintenance programs, which can reduce the financial impact of scheduled maintenance as well as minimize the “fire drills” associated with an unplanned performance degradation or interruption.

Similarly, advantages accrue to manufacturers who can capitalize on “nuggets of gold” in their data.  Better insight into operational performance can help the engineering team gain a more granular, objective understanding of the performance of their products in sustained operation across a broad range of conditions (vs. a controlled test environment) and allow them to further improve product performance and reliability.  Also, delivery of sensor or telematics-enabled products can enable suppliers to move to “power by the hour” business relationships: if the economics are structured properly, the vendor is incented to deliver reliable service and enjoy a long term, predictable revenue stream, and the customer can reduce or eliminate investment in spare parts inventories or the imperative to staff a large maintenance department.

One of the “7 Habits” Stephen Covey made famous was “first things first”.   The first step a prospective predictive maintenance practitioner can take is to understand the availability of their data and secure access to it.   Failure to take this one step has derailed or delayed more than one project.  For example, a large equipment manufacturer had to suspend a major project upon learning the telematics data they expected to use wasn’t theirs, but belonged to the telematics equipment supplier.   In another situation, a major international transportation provider, operating state of the art equipment laden with sensors collecting volumes of operational data, was stunned to learn their ability to extract and analyze the data was impeded by the equipment supplier, whose onboard system only made a handful of cryptic fault codes available when a maintenance event occurred (and prevented access to the message bus).  

The effective application of Predictive Analytics in support of Condition-Based Maintenance holds great potential.  Make sure you own your data.