Solar Builder

JAN-FEB 2019

Solar Builder focuses on the installation/construction of solar PV systems. We cover the latest PV technology (modules, mounting, inverters, storage, BOS) and equip installers/contractors with tips and tools to make informed purchasing decisions.

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Page 36 of 51

SOLARBUILDERMAG.COM | 37 S olar equipment manufacturers, EPCs and power plant owners and operators commonly face the challenge of keeping their fleet, machinery and other assets working efficiently, while also reducing the cost of maintenance and time-sensitive repairs. Considering the aggressive time- lines set for solar-focused organiza- tions, it is crucial to identify the cause of potential faults or failures before they have an opportunity to occur. By proactively identifying poten- tial issues via artificial intelligence, solar companies can turn equip- ment sensor data into actionable insights in order to more effectively deploy maintenance services and improve equipment uptime. Predictive maintenance approach The underlying architecture of a predictive maintenance model is fairly uniform irrespective of its end applications. The analytics usu- ally reside on a host of IT platforms, but these layers are systemati- cally described as: ■ Data acquisition and storage (either on the cloud or at the edge) ■ Data transformation — conversion of raw data for machine learning models ■ Condition monitoring — alerts based on asset operating limits ■ Asset health evaluation — generating diagnostic records based on trend analysis if asset health has already started declining ■ Prognostics — generating predictions of failure through machine learning models and estimating remaining life ■ Decision support system — recommendations of best actions ■ Human interface layer — making all information accessible in an easy-to-understand format Failure prediction, fault diagnosis, failure-type classification and recommendation of relevant maintenance actions are all a part of predictive maintenance methodology. As solar customers become increasingly aware of the growing maintenance costs and downtime caused by unexpected machinery failures, predictive maintenance solutions are gaining even more traction. For energy companies in particular, this type of predictive maintenance can serve as a significant competitive advantage in front of customers. The bigger players have already been using this methodology for more than a decade. Small- and medium-sized companies in the solar sector also can reap its advantages by keeping repair costs low and meeting initial operational costs. Predictive maintenance is also a step ahead of preventive mainte- nance. As maintenance work is scheduled at preset intervals, main- tenance technicians are informed of the likelihood of parts and components failing during the next work cycle and can take action to minimize downtime. In addition to the advantages of controlling repair costs, avoid- ing warranty costs for failure recovery, reducing unplanned down- time and eliminating the causes of failure, predictive maintenance employs non-intrusive testing techniques to evaluate and com- pute asset performance trends. Additional methods used can include thermodynamics, acoustics, vibration analysis and infrared analysis. What stakeholders need is a bankable analytics and engineering service partner who can help them leverage data science not only to predict embryonic asset failures, but also to eliminate them and take action in a timely manner. Sean Otto leads business development for Cyient's Advanced Analytics team. North America Predictive Maintenance Market by Vertical 2017 -2022 Solar construction contractors can improve asset efficiency with predictive maintenance By Sean Otto Auto Repair

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