Equipment manufacturer Caterpillar recently announced a new initiative to “further expand contractors’ and fleet managers’ ability to predict the future,” according to a recent Equipment World blog, Caterpillar’s predictive diagnostics will enable more ‘repairs before failure’ in future telematics updates, and CAT has “made a minority investment in an analytics firm called Uptake as part of a partnership that will see the two companies develop predictive diagnostics technology for both Cat-branded and non-Cat-branded products in the next few years.”
The use of telematics has been increasing in popularity among manufacturers for some time now. CAT’s investment in Uptake illustrates that they want to go a step further, providing their customers with maintenance predictions based on perceived correlations between fault codes and failures. The goal is to couple telematics data with analytic tools from Uptake to provide more visibility about the health of equipment in the field— but is this really enough to successfully implement preventative diagnostics? Often there is necessary information that isn’t included in telematics data.
By tracking all the data that machines produce, contractors and fleet managers have been striving to achieve accurate predictive maintenance. After all, if problems that affect equipment fleets can be quickly identified contractors should be able to catch issues before they affect equipment performance or availability. Caterpillar chairman and CEO Doug Oberhelman was quoted as saying, “we want to empower our customers with the insight necessary to shift from a reactive ‘repair and failure’ mode to a proactive ‘repair before failure’ stance.”
On the surface, the value telematics provides to contractors and fleet managers appears solid. However, with just a bit of digging it becomes clear there’s more to the story. An important consideration is that diagnostic alerts are just that—alerts not conclusions (even when based on fault-code correlations). Diagnostic alerts can be caused by many things such as an electrical glitch, a faulty sensor, incompatible software, operating temperature/environment, field modifications, equipment abuse, damage, etc. When a contractor sees a diagnostic message, all they know is that the machine thinks there’s something wrong. Oftentimes the diagnostic system works properly, alerting the owner to a faulty component before the equipment self-protects and shuts down. But just as frequently diagnostic alerts are ambiguous, requiring proper troubleshooting to determine whether or not there’s a real problem. Furthermore, even if the diagnostic alert properly points to a faulty component it rarely identifies “why” a component is failing. Since every component has multiple ways it can fail, even with similar symptoms, neither the equipment owner nor the manufacturer is any wiser about how to prevent the problem from recurring in the future. Without knowing the cause (failure mode), determining it and fixing the problem across a fleet can still be a time-consuming, frustrating and costly experience.
The problem with CAT’s approach is that telematics data alone misses other useful information that helps isolate the actual equipment problem. Human experts are often needed to weigh-in and determine exactly what has gone wrong and why so that necessary repair procedures can be established. To effectively pre-empt equipment issues, telematics needs to be combined with a tool that can accurately process diagnostic data, consider all possible causes that would generate those codes, and gather additional information that may be relevant (even from outside the data stream) to quickly identify the underlying cause of equipment issues—before they turn into full-blown failures. An integrated guided diagnostic solution will significantly enhance an OEM’s telematics strategy, to provide a holistic view of equipment that’s operating in the field and to identify the underlying causes of performance issues and component failures.