Industrial equipment downtime is detrimental to production success. Fortunately, IoT-connected businesses can leverage their data to gain an advantage. Companies using adaptive diagnostics, which provides a step -by- step repair plan that becomes smarter over time by continually learning different failure scenarios so that technicians can solve issues correctly the first time. Additionally it provides detailed information about each asset in an interactive guide to teach technicians how to fix issues according to their skill level and addresses what parts are needed for the repair to keep the process agile and increasingly efficient.

In contrast, technicians without the benefit of adaptive diagnostics are forced to go through a timely and labor-intensive process by visually inspecting the troubled asset and going through the tedious, age-old techniques of manual processes.  This traditional method results in sorting through old-school manuals and documentation for repair clues, ultimately resulting in more downtime, delays and increasing repair costs.

Adaptive diagnostics leverage machine learning and iterative data updates to gather and filter the relevant information needed to quickly and accurately troubleshoot so that technicians at all levels can make repairs quickly, helping to provide a detailed overview of the problem in real-time, and more importantly, a timely remedy.

To read more about adaptive diagnostics and its advanced troubleshooting capabilities, check out this article by Dave McCarthy for TechTarget.

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