Manufacturers of all sorts have long sought a balance between tasks best done by automation against those best done by human beings. In recent years, this has shifted in favor of automation due to more capable technologies coupled with a decline in the availability of qualified people. One area where this has been particularly visible is the traditional practice of sending out humans to perform manual inspection and data collection rounds.
These activities are where an operator with a clipboard or more sophisticated recording device follows a route through a facility with stops along the way to read a gauge, examine a sight glass, or some similar check related to a process directly, or verify equipment condition.
This may be simply a mechanistic recording of a reading, but there may also be a more subjective component. After reading and noting the gauge, another step may call for an evaluation. Does the compressor sound normal? Is the centrifugal pump vibrating more today than yesterday? Is the seal leaking? Is there a faint aroma of burning lubricating oil? Some operators may be highly qualified to make such judgements based on extensive experience, but how many of those people are around today? A generation of skilled workers is quickly fading into retirement.
In many industrial plants and facilities, the reality is changing because these rounds tend to be tedious, time consuming, and may not add much value to the operation as the result is usually simply confirmation of any obvious signs of abnormality. Insightful observations are waning, leaving the little potential for detecting a problem developing slowly. These characteristics make routine rounds prime candidates for automation, especially for far-flung equipment (Figure 1).
Fortunately, there is no shortage of appropriate instruments capable of measuring relevant variables. The larger problem is gathering data efficiently and bringing it to a central host system. Often the equipment needing monitoring is spread over a large area, potentially in places that are difficult to access or are hazardous. Distance calls for a wireless solution, but not all such systems are created equal. For the balance of this article, we’ll examine a practical way to solve the overall challenge.
Wireless deployments
While there are few hard statistics, it is safe to say that the majority of process manufacturing facilities have deployed wireless networks in some capacity. Wi-Fi networks, first set to cover offices, now extend into production areas to facilitate mobile workers and a wide variety of other purposes, but few support instrumentation or field devices of any kind.
Many progressive companies have deployed other types of wireless networks specifically for device-level support, such as ISA100.11a and WirelessHART. These networks are ideal for process instruments and actuators involved in process control, supervised by a distributed control system (DCS), and serving similar functions as wired counterparts. Although these two protocols are incompatible, they share many functional similarities:
- Similar communication distances, for example up to 500m line-of-sight for ISA100.11a.
- Extremely high signal reliability.
- Ideal for high-density installations.
So, what is the answer for an engineering team wanting to deploy temperature and pressure sensors in condition-monitoring rather than process-control applications? Either of these device-level networks is better than Wi-Fi, but there are still drawbacks:
- Condition-monitoring instruments do not need frequent updates.
- Data from a condition-monitoring instrument will have to be separated from the process instruments on the same network so it can be sent to other people.
- Equipment needing condition-monitoring instruments will likely be more spread out, so longer communication distance coverage is desirable.
- Battery life is often insufficient.
- Most installations require repeaters.
The answer is choosing a wireless protocol suited for the specific requirements of condition monitoring, rather than trying to make it fit in a less optimal solution. What does that look like?
An alternative wireless protocol
The requirements for a wireless protocol supporting condition-monitoring instruments are different than for a network supporting process devices:
- Longer distances may need to be covered.
- Lower update rate is typical, often hourly as opposed to every few seconds for a process device network.
- Lower signal reliability tolerable.
- Long battery life.
This can be satisfied by a different wireless protocol, originally designed for smart city and other industrial purposes: LoRaWAN. This low-power, wide-area networking protocol (Figure 2) is supported by the LoRa Alliance and used by 500 IoT-related companies globally.
It communicates via a license-free 920 MHz band, which works well for communication over a radius of one km from the gateway, with no repeaters required. These distances are possible even in environments where buildings, pipes, and other obstructions are present. Battery life for LoRaWAN sensors is longer, with thermocouple or pressure sensor batteries lasting up to 10 years.
Combining sensor and LoRaWAN transmitter
Since the introduction of WirelessHART and ISA100.11a about 13 years ago, the range of native process instruments has grown enormously, but sensor selections have been largely limited to devices with low power consumption.
Offerings designed for equipment condition monitoring tend to focus on tell-tale variables, including temperature, pressure, and vibration. Let’s look at how the first two can be applied.
Temperature sensors are designed to work with various types of thermocouples, including B, E, J, K, N, R, S, T, and C. The transmitter can support the full measuring range of each type, so low as well as high temperature measurements are possible. The actual temperature sensor can take any typical form and be mounted separately from the transmitter, or it can be attached directly. This allows monitoring anything from gases and liquids flowing through a pipe to electric motor bearings.
Pressure sensors for condition monitoring tend to be basic in-line gauge pressure measuring devices, which can be mounted directly like a mechanical gauge or connected to the process via impulse lines. Frequently, the new wireless transmitter can be mounted in the same location as a mechanical gauge it is replacing. Typical applications might include monitoring the output pressure of a pump or compressor, back pressure of a reverse-osmosis membrane to detect clogging, or pipeline pressure.
When installed in a plant environment, it is often possible to install a single LoRaWAN gateway in a central location capable of covering the whole facility, thanks to its one km radius range. Naturally geographically large facilities, such as a tank farm or well head site, may need more than one gateway. Since this network only serves the condition-monitoring sensors, data can be routed to the appropriate users, such as maintenance or reliability teams.
Examples of automated condition monitoring
Returning to the earlier discussion about manual rounds, let’s look at how condition-monitoring sensors improve on traditional practices, including:
- Inconsistent timing—Manual rounds are effective only when operators perform them consistently. If an emergency or other assignment causes a skipped visit, a problem has that much more time to escalate.
- Inconsistent data retention—If data has to be collected and transferred to a host system manually, there is high potential for errors and missing data.
- Inability to spot trends—With gaps in data, it is difficult to recognize trends and a problem steadily escalating.
Automated data collection ensures consistency on all sides. There are no gaps in performance and no errors. If there are trends, they are easy to spot. Since all the data comes in through a single gateway, or at least a single network, all data can be directed to the right individuals for evaluation and appropriate action. Here are some brief examples of how companies have put this capability to work.
Dust Collector—A process that generates dust, such as a lime or cement kiln, must capture dust from a flue gas stream via a dust collector or baghouse. These items of equipment can become problems if they are too full, reducing throughput, or begin leaking due to a broken bag. Placing a pressure sensor on the outlet ahead of the induced draft fan can diagnose both conditions.
Pressure-Relief Valves—Any pressurized equipment in a process unit must have pressure-relief valves (PRVs). These are notorious for leaking due to malfunction or inadequate re-seating after a pressure event. A slow but continuous leak over a long period of time can waste an enormous amount of product. Since most processes operate either above or below ambient temperature, it is possible to detect leaking by installing two temperature sensors, both tied to a single LoRaWAN transmitter, upstream and downstream of the valve. If there is no leakage, the downstream sensor should show a reading equal to ambient conditions. If the downstream sensor moves closer to the upstream reading, it is due to fluid coming through the valve. The difference between the two readings can indicate the amount of leakage.
Coal Storage—Companies that store bulk coal (power plants, steel mills, etc.) can experience spontaneous ignition due to heat build-up deep in piles. If temperature sensors are inserted into the piles, it is possible to detect a temperature increase and respond with pile agitation before it gets hot enough to ignite.
Sharing experiences and successes
When condition monitoring is automated, it can facilitate data sharing within a company from unit-to-unit and plant-to-plant. Maintenance and reliability teams can learn from each other and become more efficient and effective. Plants and even entire companies can improve output and profitability due to more reliable production through greater plant availability.
All figures courtesy of Yokogawa
About the Author
Haruka Yamada is a member of Yokogawa Electric Corporation’s IoT wireless promotion team. She joined Yokogawa Electric after earning a master’s degree in mechanical engineering, specializing in robots controlled by wireless, from Shibaura Institute of Technology. Yamada first worked as a software engineer for ISA100 Wireless, and she is now in charge of marketing and promotion of Sushi Sensor.
Referenceshttps://lp.yokogawa.com/sushisensor/
