IEN (Industrial Equipment News) is the leading resource for industrial professionals, providing product technology, trends and solutions impacting the industrial market. IEN reaches manufacturers, designers, distributors & supply chain professionals.
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Today's Designer 34 IEN / OCTOBER '16 www.ien.com P reventative maintenance programs are expensive, but downtime caused by equip- ment failures are even more costly. In this high-stakes endeavor, customers of in- dustrial equipment manufacturers are looking for more visibility into machine health and especially, predicted machine health. This phenomenon is creating an opportunity for industrial equipment manufacturers to create differentiated and smart connected ma- chines that provide granular machine health data that can be used to reduce operational expenses, increase product and process quality and improve overall corporate competi- tive position. Benefits of Predictive Maintenance Customers are looking for smart connected products that support their preventative main- tenance program by providing the following benefits: • Decrease parts cost and capital expenditures. Reducing service costs and in- creasing the overall service life of a machine reduces long-term capital expenditures. In addition, reduced spare parts inventory is possible due to increased confidence in operational health. • Increase operational efficiency. If machines are operating longer between service events, and foreknowledge of probable future issues is possible, overall operational efficiency can be improved leading to the business outcome of an improved corporate competitive advantage. • Decrease on-going compliance costs. Corrective and preventative maintenance is costly from a labor perspective. Improving visibility into issues before they happen can have a significant impact in lowering related on-going compliance costs. • Increase product and process quality. As machines are operating more efficiently and with higher quality and lifespan, the resulting quality and effectiveness of the processes that depend on machine performance also go up. Best Practices in Predictive Maintenance for Connected Industrial Equipment by Mark Benson, Chief Technology Officer, Exosite These are just some of the benefits of how predictive analytics can benefit a preventive maintenance program. However, by adding more sensors to equipment, and combined with an IoT data platform that can collect, process, and visualize large amounts of machine data from multiple machine types, locations, and applications, the following additional benefits can be gained: • High fidelity machine health. Local analytics at the sensor or device level that down-sample high-frequency sensor readings and send summarized data up to the cloud reduces latency, bandwidth, and cloud storage costs while providing granular visibility into machine performance. • Real-time stream analytics. Stream analytics that transform data or look for patterns and trends in data streams that indicate areas of concern can solve many types of data analytics problems. These include machine health-indicator problems, predictive maintenance problems, and service delivery problems. • Flexible data visualization options. An IoT platform can deliver highly customized visualization interfaces that can be tuned as needed. This is especially of interest to manufacturers of connected industrial equipment that want to provide a specific branded experience to their end customers. • Multi-machine, region, and application analytics. By collecting data from multiple machine types in multiple locations and in multiple applications, a rich set of data can be collected and analyzed using big data tools to find trends occurring that may not be apparent if looking at discrete machines alone. Data Analytics: A Maturity Model In the broader sense of data analytics, the industry is still immature. Few organizations have mature data-analytics programs in place to support their preventative maintenance programs, and there's no magic bullet—the type of analytics that help one solution succeed