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Implementing these solutions reportedly helped the Wisaforest mill achieve payback on its upgrade investment in just eight weeks.
In addition, though basic vibration has been used to check machine health for decades, new algorithms and software are helping users monitor more operating parameters and applications. For example, proximity sensors and software included in Bently Nevada’s Asset Condition Monitoring Solution can take signals that indicate the trueness of shafts in motors. “We have new algorithms and software that can set up alerts and alarms based on asset conditions,” says Jeff Schnitzer, Bently Nevada’s general manager. “We also have Rule Packs that allow users to write rules based on vibration, temperature, or other indicators, and create subsequent functions and responses.
“These and other machine health monitoring capabilities can then be combined with maintenance records, assets classifications, and other data to create baselines for entire plants, and help target future investment where it will return the most improvement. This means no longer touching or replacing components based on past outages and history, but touching or replacing them based on real-time reliability data.”
Robert Skeirik, machinery health product manager for Emerson Process Management, says it links the five main senses of machine health—vibration, oil condition, motor current, infrared thermography, and ultrasonics—into its AMS Machinery Health Manager, AMS Suite software, CSI 2130 portable analyzer, CSI 4500 analyzer that connects to a multiplexer, and CSI 9210 machinery health transmitter.
Skeirik adds that Emerson and its CSI division recently helped upgrade the 20% worst-performing of 27 coal pulverizers at American Electric Power’s (AEP) John E. Amos plant in St. Albans, W.V. The units were previously serviced on a 12-month schedule, and collectively experienced 3-4 unexpected failures per year. Besides preventing $240,000 worth of breakdowns in the project’s first year, CSI and Emerson helped AEP and Amos’ engineers achieve savings and return on investment (ROI) in several machinery health monitoring areas:
“Machinery health is now just one component of a plant’s overall status, which is continually seeking to answer the plant manager’s two basic questions about reliability and availability,” says Skeirik.
An Ultrasonic Heartbeat
Though vibration detection and analysis is a bread-and-butter machine health monitoring method, and one that’s also increasing in sensitivity and sophistication, some developers believe ultrasonic monitoring may be even more useful. Based on military and aerospace monitoring methods, Stress Wave Analysis (SWAN) technology from Swantech reportedly can detect even earlier when a machine is starting to demonstrate symptoms that will lead to failure (See Figure 2 below).
FIGURE 2: SOUNDING STRESSED
Ultrasonic stress wave energy (SWE) plots for two machines show the one on the right was damaged in a “seeded fault” by an insufficient quantity and inappropriate quality of lubricating oil. The plots indicate the SWE levels on the “faulty” machine were immediately higher than its twin, trended upward, and accelerated, as the machine accumulated actual wear and damage.
“Ultrasonic technology provides the earliest detection of machine problems by detecting the unique sounds made by friction, impact events, and minor surface damage,” says Ralph Genesi, Swantech’s president and CEO. “Traditional machine health has been based on vibration, but this is too late because some damage may already have occurred and a safety shutdown may be needed. Ultrasonic detection gives users more time to gain the knowledge to plan maintenance and a response, and lets users decided if they need to shut down now or if they can safely run their planned quota.
“In addition, electrical signal histories collected over time, or histograms, can show if new signals are skewed, and even help indicate if a machine has lube problems, cracks, or seal damage. Ultrasonics can even show when an external load is placed on a machine, which can help operators change bad habits, and extend the lives of their devices.”
For example, to help it process more than 2 million tons of reclaimed steel per year, North Star BlueScope Steel in Delta, Ohio, recently asked Swantech to help maintain its flat roll machines, which usually run 24/7 and can cost $1,000 per minute of unplanned downtime. The steelmaker’s SWAN system gathers information from Stress Wave data collectors associated with each bearing, analyzes it on a SWANserver in the plant’s offices, reports on any early signs of wear in the mill’s equipment, and allows managers to plan maintenance or repairs up to six months in advance.
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