The industrial internet of things (IIoT) and Industry 4.0 are already unleashing enormous value in plants around the world. It seems that today’s younger, digital workforce is the energy that propels this change. Past efforts had been sluggish to say the least.

Modern consumer products have put connected devices in our pocket, on our wrist, in our ears, in our car and throughout our home. IoT is projected to deliver between $1.9 and $4.7 trillion of economic value by 2025. The IIoT for asset monitoring is expected to produce $200-$500 billion in economic value by 2025. Condition-based maintenance (CBM), involving real-time sensing and predictive maintenance, is viewed as the “easy win” among all IIoT applications. Many new online sensors are being introduced each year (see Figure 1).

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The advent of unattended sensor technologies has placed a particular challenge on data communication for machine surveillance monitoring. This challenge is magnified when mobile equipment and large geographic territories are involved. The use of satellite communications has recently been applied in an extensive condition-based maintenance program for a large fleet of railroad maintenance equipment. The satellite communications. are a part of an integrated system of sensor, software and communications technologies.

This paper describes the selection and application of sensors for the real-time monitoring of contamination in hydraulic fluid and engine oil. Also discussed will be onboard data collection electronics, onboard satellite transmission hardware and knowledge-based data analysis and exception reporting. The paper will be presented as a case study, and is believed to be the first of its kind for condition-based maintenance employing satellite communications technology.

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Condition control is defined as the interactive processes of condition monitoring, condition analysis, and condition response. A model is presented which employs the use of expert systems to achieve real-time condition control of a hydraulic system. The approach focuses on the use of fuzzy logic to achieve machine intelligence to monitor and analyze pre-degradation ‘incipient’ failure and impending conditions. Also discussed are the corresponding real-time ‘reactive’ condition responses, coupling system control with condition control.

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Somewhere in your plant there are all-important pieces of production equipment that simply must operate as intended-or else. And most likely the “or else” is too painful to even think about: injuries, catastrophic damage, contract penalties, irate customers, missed shipping dates, junked parts, ballooning costs, a sickly quarterly financial report, and so on. Those critical types of equipment are candidate for machine health monitoring.

To be more precise, the ideal candidates for machine health monitoring is one that is both expensive and sophisticated, provides a crucial and depended-upon work function, has extensive maintenance requirements, and presents a safety risk upon failure. While all mechanical systems don’t involve all those terms, many do meet one or more. And the advent of new sensor and computer technology has more than ever before made machine health monitoring a viable and cost effective technology that manufacturers can use to their advantage particularly today when the almost universal goal in manufacturing is to produce high-quality work at the lowest cost and at the most productive rate a machine can deliver.

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