Condition Monitoring Barriers Dropping
EP Editorial Staff | September 18, 2020
Advances in data acquisition and analysis mean maintenance and operations teams can spend more time improving reliability.
By Chris James, SKF
For as long as there have been machines, people have understood the need to monitor and maintain them. While the principle of industrial plant asset management remains constant, the methods have evolved from reactive to predictive maintenance.
The future of maintenance strategies however takes this even further. Predictive maintenance uses insights gained from machine data to forecast when a problem is going to arise with a machine, allowing maintenance to be performed before the problem escalates to failure. At the heart of this is condition monitoring, i.e., the practice of collecting and analyzing data on specific operating parameters.
Manual or automated?
Companies can collect machine condition data in two ways. The manual route involves equipping maintenance teams with handheld devices they can use to measure and record parameters during routine “walkaround” inspections. The automated approach involves installing permanent sensors on assets that transmit data across a network.
Choosing the correct approach for any given asset requires an organization to balance the costs of collecting, communicating, storing, and analyzing the data against the reliability benefits the data delivers. In practice, that means many organizations use permanently installed systems in their most critical assets and rely on handheld data collection for the remainder.
Changing the game
When it comes to condition monitoring, the digitalization process we’re experiencing in industry transforms both sides of the cost-benefit equation. It will drive a big shift in the way companies collect data from their machines and in what they do with that data once they have it.
First, the cost of permanently installed data-collection systems is becoming less expensive. In part, that’s thanks to the development of robust, inexpensive sensors and processing electronics. More important, it’s because connecting those sensors has become cheaper and easier to do. That matters because installation labor, along with dedicated cabling and communications hardware, can make up 60% to 75% of the total cost of a permanent condition-monitoring system.
Today, companies have several options to reduce data-collection costs. They can connect data acquisition devices directly to their existing wired networks or they can go wireless using secure wi-fi networks that are increasingly common in factories and other industrial facilities. A new generation of low-power wireless “mesh” network technologies makes it possible to install sensors that can operate for years on battery power alone. While deploying these sensors is easy, the energy budget still needs that balance against the asset’s criticality and wired alternatives.
The latest wireless condition-monitoring systems are improving rotating-equipment performance programs on a scale that was previously widely considered to be uneconomical. This is being achieved by combining the knowledge gained on machine health monitoring over many decades with emerging and innovative network technology from connectivity specialists.
One such example is a wireless condition-monitoring system that can economically automate vibration data collection. With this solution, a mesh network protocol enables sensors to exchange data, navigating around obstacles such as pipework and liquid storage tanks, instead of trying to punch through them.
A cognitive co-existence technique scans the radio spectrum and switches frequencies to avoid busy channels and overcome interference. This increases radio reliability and significantly reduces the demands on the battery in a small device. It minimizes energy usage by knowing exactly when to switch itself on and off. This means it can work on a single battery for many years, in tough wireless environments such as paper mills.
From a practical perspective there are several benefits. The self-forming sensor network requires no existing infrastructure, such as wi-fi, and can be deployed on a scale sufficient to cover the monitoring points of today’s walkarounds. Predictive-maintenance programs can be expanded, with data captured more often, which increases defect detection rates and leads to avoidance of costly unplanned machine shutdowns.
Today’s advanced condition-monitoring systems are also becoming less expensive to own and operate, thanks to the development of new analytics approaches such as machine-learning technologies. These methods are automating the interpretation of machine condition data to a much greater degree than was previously possible. That means companies can monitor more assets with fewer skilled analysts.
Finally, new technology is changing the way machine condition data is used. Although data analysis at a central location—or completely remotely—is nothing new, internet and cloud computing have made it orders of magnitude easier and less expensive to implement. That can deliver significant benefits for organizations with multiple assets operating around the world. The same technologies make the results of analyses far more accessible, i.e., a factory manager can now see the status of the facility at a glance on their phone.
Does digital transformation mean an end to the age-old tradition of the maintenance walkaround? Absolutely not. Machines will still need people to maintain, diagnose, and improve them. When it comes to routine inspections and root-cause problem solving, there is no substitute for a hands-on approach. Tomorrow’s maintenance specialists will probably spend just as much time on the shop floor, but they’ll spend less of that time performing routine checks and measurements, and more on activities that deliver real performance and reliability improvements. EP
Chris James is Product Line Manager for permanently installed condition monitoring at SKF Group, Nieuwegein, Netherlands. He is responsible for wired and wireless online technology products that support SKF’s digitization strategy.