Sensors and systems for CBM

Condition-based maintenance (CBM) holds the promise of predicting machinery maintenance requirements based on process performance measurements. A CBM system can minimize the maintenance actions on equipment without affecting system availability or reliability. CBM provides an efficient, cost-effective maintenance method by using sensing technology, signal processing and software techniques.

Condition-based maintenance allows you to take planned corrective measures following sensing and identification of machinery performance degradation. The primary benefit of a CBM system is increased availability (uptime) of plant machinery and equipment. Other benefits include lower maintenance costs by reducing preventive and corrective maintenance hours and the prevention of secondary damage by detecting potential machinery failures.

Historically, because of costs involved, condition-based maintenance has been applied to large rotating machinery such as motors, pumps, generators, compressors and similar machines. Sensors are the first link in a successfully implemented condition-based maintenance system.

Sensing Techniques for CBM
Traditional sensors used in large-scale applications of CBM could include the following types of devices:

Vibration sensors (accelerometers) measure the motion of the machine and identify mechanical faults that are developing, such as misalignments in driven equipment or failing motor mounts.

Flux coil and current readings monitor electrical conditions. Increased current levels could indicate bearing wear in a conveyor system or a sticky (gummy) belt.

Temperature transducers, such as thermistors, resistance temperature detectors (RTDs) and thermocouples, monitor the ambient temperature and the motor surface temperature. They can determine motor overheating conditions and indicate high frame temperatures caused by excessive bearing wear.

Thermal imagers – handheld, non-contact units – are devices used to scan and save the temperature and infrared image of production equipment. This data is useful for comparing abnormal and normal operations. Heat can be an early indicator of machine damage or malfunction.

Ultrasonic transducers detect leaks and inspect mechanical and electrical components.

Expanding Applications of CBM
With the lower cost of sensors and increased embedded processing power available today, more applications for CBM are now feasible and cost-effective. Many electrical devices now incorporate sensors that can provide performance data along with their basic control functions. For example, users of servos and other drive systems can implement predictive maintenance algorithms that monitor the output torque of the motor by sensing the current output of the drive (see Photo 1).

Photo 1. Louver production machine containing a servo drive system.
(Photo courtesy of G & L Technologies)

Once the torque required at each point of a “normal” machine cycle is known, it’s fairly simple to monitor this torque profile and alert maintenance personnel to any abnormalities. Increased torque output can indicate a bearing that is beginning to fail or other impending mechanical problems. With this knowledge, you can repair the mechanical equipment during the next scheduled maintenance period, as opposed to making repairs after an unanticipated breakdown. Less sophisticated types of motion control (i.e. stepper systems) that do not have torque-sensing capability cannot provide this type of information. It may be more efficient in the long run to install a more capable drive based solely on its ability to assist in predictive maintenance.

Sensors used to monitor machine or process conditions can report data, but, ultimately, it is the system to which they are attached that provides the intelligence to interpret the data and take action.

An example of condition-based maintenance using a logic platform is a pH measurement management system. An application is an integrated auto-cleaning, auto-calibrating, auto-diagnostic system for pH sensors installed in a live process. A programmable logic controller (PLC) automatically tests, cleans and calibrates pH probes in applications where the probes are exposed to abrasive or caustic conditions. To measure drift and efficiency, the PLC retracts the pH probe from the process, injects known pH buffers and reads the 4-20 milliamp (mA) inputs. The PLC is able to compensate for drift (bias/shift) and decreased efficiency (slope) over time to log and report an accurate pH measurement. Based on the curve of performance degradation, the system could also predict when the probe will require complete replacement.

The Open Connectivity CBM System
Distributed monitoring systems today have moved toward architectures based on open standards for both hardware and software. PLC- and PC-based platforms communicating to distributed input/output (I/O) devices and sensors over a variety of accepted fieldbus networks have made their way into CBM applications.

OPC, or OLE (Object Linking and Embedding) for Process Control, is an industry standard created by a number of leading hardware and software suppliers in cooperation with Microsoft. Using open connectivity technology, live data from industrial devices can be communicated to upstream systems or a Web page with no programming required. Data is then available for viewing, printing or archiving on any computer, or anywhere the Internet or company intranet is accessible. OPC technology allows easy and inexpensive data collection and display because it is supported by so many different devices. As an added benefit, OPC software is able to offer a more unified approach to data display and logging for plants that use multiple PLC brands (see Graphic 1).

Graphic 1. OPC-based data collection from a control system provides useful data for maintenance decisions.

OPC software offers many useful features designed to make monitored data more accessible and useful. For example, dynamic colors allow users to determine the status of their process operations at a glance. Use these as an early warning system to alert personnel of abnormalities in operations. Math functions can perform calculations on the raw data before it is displayed or processed further. Many off-the-shelf OPC-based CBM programs are now available to manage maintenance efforts.

Conclusion
When implemented correctly, a condition-based maintenance system will help lower maintenance costs, increase machine availability and reliability, improve safety, enhance product quality and, in many cases, extend the life of the equipment. Carl Hamilton is a technical specialist for AutomationDirect, a direct seller of automation and industrial control products. For more information, call 800-633-0405 or visit www.automationdirect.com. Learn more about automation. A glossary of common automation terms is available by e-mailing Reliable Plant editor Paul V. Arnold at parnold@noria.com.