Key points
Plants such as water treatment facilities and food and beverage factories rely on a large range of process instrumentation – quite obviously, without accurate readings of parameters such as flow rates, pressures and temperatures, their operations would grind to a rapid halt.
Devices such as flow meters and pressure transmitters perform this vital work, but is enough attention paid to the health of these instruments – who monitors the monitor and how is it done in a way that improves the efficiency and performance of the whole plant?
Today’s process instrumentation is largely digitally based. As such they produce a wealth of information, not only about the behaviour of the parameters they are designed to measure, but also about their own performance and accuracy. Many modern devices can check their own circuitry, help with their own calibration and evaluate their own performance.
Yet, these capabilities are not as widely used as they could be. Many operators are not using this available data to its full extent and are therefore missing out on the revolution in device diagnostics and maintenance that has happened over the last four decades.
Originally, most plant operators took a reactive approach, where essentially a device was allowed to break and then was fixed. This was an expensive method as in certain cases it could lead to the whole plant tripping out, extended down time and a rush to find replacement parts or devices.
For example, in a batch production environment, such as mixing orange juice concentrate with liquid in a food and beverage plant, the failure of a flow meter would not allow the right proportions to be mixed to produce the correct quality of product and the process would need to stop until the problem was resolved.
This is bad enough in the above batch method but in a petrochemical process, based on 24/7 continuous production, allowing a temperature transmitter to simply fail could be potentially catastrophic.
Things got better in the 1970s to 80s, when preventive maintenance came to the fore, with devices maintained to a schedule. Even this was highly manual, with equipment being checked by staff walking the line to note which devices were reading or behaving as expected and which ones needed attention.
Over the last few years, the concept of predictive maintenance has become ever more popular, with the aim of trying to prevent failure events in the future. Although predictive maintenance is still a form of preventive maintenance, it uses actual condition-based data to determine when to perform maintenance, as opposed to a pre-determined schedule based on historical information.
By using condition monitoring of equipment, we can know more about exactly when to schedule maintenance and prepare for it in advance, for example, ordering spare parts. We can also make use of the increasingly rich data available from measurement devices to identify exactly what went wrong and derive strategies or techniques to prevent the same problems occurring in the future.
There are two main reasons for condition monitoring. The first and most obvious is that we want to check the device’s health. If it is going wrong, perhaps suffering a fault that is causing it to stray outside its calibration parameters, this could affect production quality by causing problems such as inaccurate blending, dosing or billing. The other reason, even more important, is ensuring that processes remain in a safe condition.
Using the data
So how can measurement device users begin to take advantage of these capabilities?
At a very basic level, it comes down to how we communicate with the device. Although most measurement devices are digital, many applications continue to use the tried and tested 4-20mA current loop rather than digital communications networks.
For example, all ABB’s 4-20mA measurement devices also offer HART communications, a bi-directional communication protocol that provides data access between intelligent field instruments and host systems. The communications link carries the process values, like flow rates or temperature.
It also gives access to the device’s diagnostics data, such as its operational status. Many advanced digitally enabled measurement devices will offer sensor checks including internal connections, an indication of sensor memory failure and an internal electronics check.
With communications in place, device interrogation is the next step. There are two methods for interrogating device data.
The first is inbuilt condition monitoring and diagnostics based on the NAMUR NE 107 standard. To make things easier for technicians and other process personnel who need to deal with alarms, NAMUR NE 107 categorises internal diagnostics into four standard status signals — failure, function check, out of specification and maintenance required, each of which can also contain greater detail. For example, with a failure signal, can the failure be traced to the device, or alternatively, is the process itself at fault?
Devices with NAMUR NE 107 diagnostics built in allow users to turn off diagnostics that are not required or configure how the diagnostics are reported.
Another option is instruments that encrypt device maintenance and operating conditions within dynamic QR codes. This feature makes it easier for less experienced personnel to streamline troubleshooting processes, as they can simply take a picture of the QR code with their smartphone.
Maintaining accuracy
One of the most important aspects of maintaining a device’s accuracy is field verification. Although much condition information is available through the HART connections, this does not provide enough detailed diagnostic information to determine if the device has drifted away from the parameters set at the factory or during commissioning.
Manual verification, performed by hand using multimeters and similar devices, has numerous drawbacks. As well as requiring longer downtimes, it also needs technicians to be specially trained, as well as risking further, unplanned downtime if not performed properly.
Software verification is faster, but this often entails using different software packages for different types of devices. There is also the problem that one type of verification software does not work with all communications protocols.
The best modern verification software packages can be used for many device types, comparing their settings to when they were first commissioned or calibrated and determining if their performance has degraded since that time.
These software packages perform a deep analysis of the device’s condition, with no need for any specially trained technicians. A report is produced on the calibration variables, assessing the limits and determining if the device has passed or failed on that parameter.
An example of the benefits possible from automated versus manual verification is an organisation that used the technique to achieve an annual reduction in costs of over $1 million.
Operating 75 measurement devices, the organisation originally spent $150,000 annually on verification for regulatory purposes, while annual maintenance and repair on the devices came to $100,000. It also lost five percent of its production due to poor quality.
By moving to automated verification, the company reduced total maintenance by a minimum of $162,000 and increased throughput by $130,000.
Making your move
Whether a company moves towards more use of condition monitoring depends on several factors, including their current device set up, their operating philosophy, their perception of value and what they want from a potential supplier.
For example, the size of the plant may be a factor. Some process plants may have thousands of devices. Obviously, those used on safety critical applications will receive the most attention, for example the pressure transmitter that opens a valve when a pressure vessel gets close to its maximum pressure limit.
Yet not all a plant’s many devices are monitoring a critical process. It may be more economical to settle for reactive maintenance of these devices – they are usually very robust, with a life of some 10-20 years so there is an argument that running these less critical devices to failure is a viable philosophy.
In the same plant, there may be other devices that really must be monitored for their health and accuracy, such as instrumentation, gas analysers or wastewater analysers used to comply with regulatory standards.
Cybersecurity is also a consideration. For automated monitoring, companies will need to feel confident that a chosen supplier has both the device and network expertise to keep data secure. Larger, more established vendors will have decades of experience in securing data from critical infrastructure such as power grids and refineries – by contrast, startups may have some exciting technology but may not be able to offer the secure services at scale that many users will be looking for.
Device OEMs should also have the expertise to take data from the operational level to the enterprise level, so that organisations can see the economic impact of device monitoring strategies and how they affect the success of the wider business.
Ultimately, it is a cost versus benefit analysis. Yet, with very few purely analogue devices in existence and the increasingly rich data sets now available from digital instruments, using digital diagnostics to improve accuracy, cut maintenance costs, boost quality and productivity and ensure greater compliance to regulations makes increasing sense.
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