March/April 2020

| 28 | March/April 2020 | SOLUTIONS | U nplanned downtime can cost thousands of dollars per hour. One study in 2017 found that companies reported an average cost of $2 million due to downtime alone, and unplanned downtime is significantly more costly than planned maintenance because a machine must be offline while it’s diagnosed, parts are ordered, and repairs are made. The continued operation of machines within specification, and the expected remaining lifetime of machines, is impacted by variables such as the time in operation, varying loads and operating environments, and damaging events. Condition-based monitoring seeks to quantify these impacts, provide alerts when immediate attention is required, and accurately predict when intervention will be required. Every machine is different, and every machine will age in a different way, although the aging process is usually slow and imperceptible. Unless we The wearable for machine health: Condition-Based Monitoring Condition-based monitoring (CbM) is the Industry 4.0 equivalent of wearable fitness devices. With the explosion of connectivity comes the opportunity to observe the physical world like never before and to see physical processes in action, in real time, in fine detail. In industrial systems, one of the processes important for us to understand is the process of the aging of equipment and machines. This is important in diverse markets from oil and gas, wind power generation, and industrial process control, where capital equipment costs are high, and downtime is costly. Stuart Servis, product applications engineer at Analog Devices, reports. actively look for indications of subtle changes over time, aging may go unnoticed for some time. Then suddenly there’s a failure, possibly catastrophic, that means that the machine is offline and in need of repair. End users are demanding earlier notice of impending failure to plan for downtime well in advance. They are also looking for indicators of more subtle changes in the machines that may affect the quality of their end product, such as paper and sheet metal. The combined need for earlier and earlier indication of machine wear-out and information about machine output quality drives the need for more sensitive and more ubiquitous sensing. Measurement types are also broadening, with sensing modalities such as temperature and vibration being supplemented with acoustic, motor current, and voltage measurements. These measurement systems are being combined to give a more holistic view of the state of equipment. This leads to increasing numbers of measurement channels per machine. The individual measurements often need to be well synchronized to show relationships, such as between x-, y-, and z-axis measurements of vibration. This need for synchronization further increases the complexity of systems. The increasing spread of measurement nodes and modalities means that manual inspection and measurement routines, based on manual resources, are no longer able to keep up. Systems must be deployable across the factory floor or remote site, with connectivity using existing wired infrastructure or wirelessly using robust and secure wireless systems. Bulky and expensive sensors and aggregator units must be smaller, cheaper, and more power efficient to fit in these environments. There are new, precise solutions at the component and subsystem level