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Even if high uptake causes the cost of predictive maintenance systems to drop, the systems will still remain expensive and their usage, restricted to high-cost production machinery. Even though some vendors look to the technical capabilities, future adoption of the systems will primarily depend on the price.
New analysis from Frost & Sullivan (http://www.technicalinsights.frost.com), Advances in Intelligent and Predictive Maintenance Systems, finds that predictive maintenance systems can be grouped together by experienced professionals, but numerous turnkey products are also available even though the cost remains a significant adoption factor in the industry.
The economic benefit of predictive maintenance systems will become apparent only in the end. It will help machine owners save costs on expensive maintenance by giving the service personnel an early warning and preventing unforeseen downtime of machinery. It eliminates the need for periodic checks by alerting the concerned workforce whenever there is a significant deviation from the normal course of functioning.
Predictive maintenance systems use various sensors to record physical parameters such as vibration, temperature and pressure. The parameters are then logged and compared to historical data from the machine in order to check for variations. For instance, a change in the vibration signature could indicate a bearing malfunction. This prompts the personnel to trace and replace the faulty component.
“Economies of scales as well as advances in sensing technologies can alleviate the cost issue to some extent, making it more affordable to run predictive maintenance systems,” says Technical Insights research analyst Sivam Sabesan. “The availability of microelectromechanical system (MEMS) sensors, for instance, has improved many maintenance systems, thereby boosting their popularity.”
Rapid improvements in sensors have also facilitated the creation of more sensitive maintenance systems. In fact, MEMS technology has aided the development of solid-state sensors, which perform better and are cheaper than conventional sensors.
MEMS sensors’ production relies on the same fabrication process as microprocessors and the evolution of microprocessor fabrication that has resulted currently in unachievable capabilities.
“Fabs of 45 nm and 90 nm allow sensor manufacturers to develop even better sensors,” observes Sabesan. “This also gives rise to a number of fabless sensor manufacturing companies that merely design the sensor and get the manufacturing outsourced, enabling them to reach the market quickly and cost-effectively.”
The availability of tremendous computing power has enabled the addition of processing power to units not just centrally, but also locally. The local processing units and minimal storage will allow the comparison of data from sensors with benchmark values. Moreover, with processing power increasing with every generation, monitoring systems have become even more sophisticated, but remains the same price.
“If the transmission cost is high, then only exceptions and alerts can be communicated, with 'normal' data ignored,” notes Sabesan. “This kind of capability is impossible in a 'dumb' monitoring unit, which only relays data to a central processing unit.”
Advances in Intelligent and Predictive Maintenance Systems, a part of the Technical Insights subscription, provides a technology overview and outlook for sensors that aid predictive maintenance systems. The study covers sensors, signal processing, and communication modules. Further, this research service includes detailed technology analysis and industry trends evaluated following extensive interviews with market participants. Interviews with the press are available.
If you are interested in an analysis, which provides manufacturers, end-users and other industry participants with an overview, summary, challenges and latest coverage of advances in intelligent and predictive maintenance systems, send an e-mail to David Escalante, corporate communications, at email@example.com with your full name, company name, title, telephone number, company e-mail address, company website, city, state and country. Upon receipt of the above information, an overview will be sent to you by e-mail.
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