Making the Right Maintenance Decisions with Intelligent In-Line Sensors
- Stefan van der Wal, Chemical Segment Specialist,Mettler Toledo Process Analytics

Plant safety and process reliability often rely on analytical field instruments operating correctly, which means sensors must be kept in good condition. This requires regular maintenance and servicing. But knowing exactly when to maintain, service or replace a probe has been very imprecise. Cutting-edge measurement systems that utilise intelligent sensors take all guesswork out of maintenance decisions. This technology also reduces the possibilities of human-error and lowers production costs.

Failure of analytical in-line sensors in a process can lead to poor product quality, over- or underuse of reagents, compromise equipment integrity, or cause production downtime. And if a sensor is required for safety purposes, its breakdown can have devastating consequences. Therefore, instrumentation engineers spend a great deal of time ensuring measurement points are operating reliably. However, it has been estimated that as much as 60 per cent of sensor maintenance is conducted needlessly due to it being conducted to a fixed schedule.1 And, despite all this maintenance, half of industrial accidents are maintenance related.2 Further, 20 per cent are due to human error.3

The goal for chemical production companies therefore, is to be certain that maintenance on individual instruments is actually required and to replace failing sensors before processes and safety are affected, but not so early as to lead to additional sensor replacement costs.

Until recently, deciding if and when to perform sensor maintenance or replacement has been based on a combination of past experience and speculation. Now, a new generation of intelligent digital sensors is taking the guesswork out of maintenance. Further, they are simplifying workflows, reducing measurement point downtime and increasing process reliability.

The advantage of digital signals
There are now many brands of digital sensor available. These output the measurement signal digitally for display and forwarding by a transmitter. This is in total contrast to analog sensors which output a low voltage or ampage signal to an instrument that converts the signal into a parametre value. Such analog systems can be susceptible to delivering unreliable data. This is frequently due to environmental conditions such as electromagnetic fields generated by neighbouring equipment and high humidity. Their presence can severely degrade analog signals and the longer the length of cable from sensor to transmitter, the greater is the potential for signal damage.

Digital signals are largely immune to such fields, humidity and cable length. Therefore, the digital signal output by the sensor will be received at the transmitter, unchanged.

Intelligent sensors
| But a more reliable signal is only the start of the advantages offered by intelligent sensors and compatible transmitters. The more sophisticated pH, ORP, DO, etc, sensors carry an in-built microprocessor that enables a range of significant operating advantages including fast, error-free start-up; and more importantly, diagnostics that understand the effects of the process and actually predict when sensor maintenance or replacement should be performed. These advances are nothing short of a revolution in process analytics and are helping to pave the way for the digital chemical facility of tomorrow.

Among the capabilities of the microprocessor in such pH sensors is its ability to retain calibration data. This negates the need to calibrate the sensor at the measurement point, a process that can be inconvenient at best and dangerous at worst if the measurement is located in a toxic process. Further, due to the required position of measurement points, the accompanying transmitter may be located many metres away. Such a setup may require two technicians involved during calibration: one to hold the sensor in buffer solutions and the other to operate the transmitter.

Because these sensors store their own calibration data, they can be calibrated away from the measurement point in any convenient location such as a workshop. The sensor can be calibrated using a compatible transmitter or, for the more advanced ones, by connection to a standard PC/laptop using appropriate software. Once calibrated, the sensors can be stored until required. When an installed sensor needs maintenance or replacement, it can be exchanged with a sensor that has been pre-calibrated. Upon connection, the calibration data is uploaded to the compatible transmitter which then configures itself without any operator intervention. This hot-swapping of sensors means measurement point downtime during maintenance is almost eliminated.

This approach offers additional benefits. If the sensor in the maintenance shop is stored correctly in KCl solution, it will have time to rejuvenate, which can significantly extend its lifetime and improve its speed of response. Rotating two such intelligent digital pH sensors in-and-out of a process means sensor lifetime can be twice as long as for two analog sensors, plus measurement performance will be increased.

Predictive maintenance
If the data from a measurement point is unreliable, production and safety can be severely compromised. Unreliable data can be seen on transmitters as rapidly fluctuating or totally erroneous readings. As mentioned, false data can be caused by interference to analog signals, but it can also be due to a sensor’s diaphragm becoming blocked or the sensor approaching the end of its reliable operating life. A significant problem for maintenance engineers is not knowing when sensors needs calibrated, replaced or simply cleaned. This burdensome issue has also been solved with intelligent sensors.

Sophisticated algorithms constantly run in the background while a sensor measures the process parameter. These algorithms monitor conditions in the process and the sensor’s ‘health’ and using current and past conditions are able to determine when calibration or cleaning should be performed and, in the case of a pH probe, when the sensor should be replaced. As process conditions alter, so do the diagnostics to provide continuously reliable data.

These diagnostic tools can be displayed as easily interpreted counters, on the transmitter or monitored remotely on asset management software or even on a handheld device. This ability to see when sensor maintenance will be required means needlessly servicing or replacing a sensor becomes a thing of the past. And it increases process reliability and safety as the chance of a sensor failing in the process without providing forewarning, has been removed.

Some sensor diagnostic tools may take days to adjust to process conditions before they provide reliable data. The latest intelligent sensors have resolved this by quickly learning from process conditions and are able to provide dependable diagnostics much earlier.

Simplified traceability
Recording sensor calibration and maintenance information provides useful data for instrumentation engineers. However, manually recording the information is time consuming and can be subject to human error. Intelligent sensors store this data and it can be easily transferred to software to be retained for future consultation or printed out if required.

Conclusion
Chemical plants have two main areas of concern: production efficiency and quality; and plant, staff and environmental safety. In-line process analytics often has a significant role to play in both respects.

Achieving the best performance from inline sensors demands that they be kept in good operating condition. However, sensor maintenance is often conducted to a fixed schedule, meaning that a probe might be cleaned, calibrated or even replaced when it is not necessary. This costly use of resources is due to lack of information as to what tasks actually need to be performed and when.

The highly informative diagnostics available from intelligent sensors combined with the simplified traceability they provide, offers efficiencies in maintenance planning, plant safety and productivity while also reducing production costs.