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Home > MEMS / sensing technology > CbM Applications and Sensors

CbM Applications and Sensors

Update Time: 2022-08-12 14:23:10


As industrial scenarios place more and more emphasis on monitoring equipment, sensors are required to provide predictable condition monitoring of equipment. Condition-based monitoring (CbM) is a type of predictive maintenance that uses different sensors to monitor the equipment's condition.

In the case of motors, for example, bearing damage is a common fault encountered during use, and vibration and acoustic pressure sensors are the most used to detect such faults. When the motor is powered, the rotor, winding and other faults are mostly measured by current transformers.

CbM and sensing

There are many motors applications in industrial scenarios, and let's look at the role of sensors in them with the condition monitoring of motor equipment. Several major types of information need to be measured by sensors in this scenario: vibration, boost, motor current, magnetic field, and temperature. Vibration signals are generally detected using accelerometers. Several common faults in motors are closely related to vibration, such as bearing condition, gear meshing, pump cavitation, motor alignment condition, motor balance condition, and motor load condition. Piezoelectric accelerometers and MEMS accelerometers are very common in this type of detection. Piezoelectric accelerometers are low noise and high frequency and are suitable for many applications. In contrast, MEMS accelerometers are low cost, small size, low power, and frequent visitors in CbM.

Microphones naturally achieve the detection of sound pressure. Ordinary microphones and ultrasonic microphones are not high in cost, small in size, and do not take up much of the budget in terms of power, and the difference is mainly in frequency. The higher frequency limit means ultrasonic microphones can detect equipment faults such as pressure leaks. In contrast, ordinary microphones mainly detect faults such as bearing status, gear meshing, pump cavitation, motor alignment status, motor balance status and motor load status.

Condition Monitoring Development Platform, ADI

Motor current is measured using traditional shunt methods, which are inexpensive and not invasive to the device circuit itself. It can detect eccentric rotors, winding problems, rotor bar problems, power supply imbalance problems, and bearing problems when the motor is powered. Magnetic field-related detection is also familiar Hall, magnetometer these sensors, low cost and size are not large, in the industrial temperature range can also maintain stability, more used to detect the rotor bar and end ring problems.

Temperature detection routine, we will first think of RTD, thermocouple, and digital temperature sensing these sensing. These sensors are now low-cost small in size, and also accurate enough to be able to friction, load changes, excessive start-stop, power supply and other temperature changes caused by sharp perception. If the requirements for temperature detection are very high, want to detect changes in the location of the heat source due to load changes, excessive start-stop, power supply shortage, etc., infrared thermography is more appropriate. However, this sensing one-time equipment is costly but can achieve accurate CbM.

How should sensors be selected for CbM vibration detection?

In the state of the monitoring based on the selection of sensor standards is the choice of high-performance devices, but this high performance in the high where or to be divided into different detection objects to determine. For example, vibration sensors must have low noise and wide bandwidth in bearing detection, the two most important performances. Of course, the benefit of wireless installation to promote the rapid growth in recent years, the size of the sensor, integration and power consumption is also a large part of the considerations.

In vibration detection, both bearing defects and gear defects require vibration sensors with noise control below 100 µg/√Hz, while the bandwidth needs to be above 5 kHz; otherwise, failures that occur in bearings and gears cannot be sensed, and bearing defect detection will additionally require a higher range of sensor g values. Because such faults are not very visible at first, especially in the early stages, they are difficult to identify by increasing the vibration frequency alone, and vibration sensors with low noise and wide bandwidth must be paired with high-performance signal chains, processing, transceivers, and post-processors for complete monitoring.

Moderate noise requirements above 100 µg/√Hz are suitable for unbalance and motor misalignment detection, where a bandwidth of 5× to 10× fundamental frequency is required for such fault detection. Unbalance and motor misalignment faults may require the sensor to be capable of multi-axis detection, whereas unbalance faults may require a low-frequency response from the vibration sensor for slow rotating machines.

MEMS Accelerometers, ST

In need of high bandwidth and low noise vibration detection, piezoelectric accelerometers and MEMS accelerometers both have the capacity and low noise to meet the conditions. Suppose the pursuit of extreme performance, piezoelectric bandwidth, and noise performance are better but more expensive than MEMS. In that case, MEMS accelerometers can provide DC response and comes with a self-test function that can verify the availability of the sensor itself. After all, MEMS modules typically contain ADCs, processors and filters tuned to the sensor to optimize performance and save space requirements for the signal chain. It should be said that the advantages of smaller size and higher integration of MEMS accelerometers are also obvious.

Final words

Choosing the right sensor is very important in CbM applications. A proper sensor selection can detect, diagnose and even predict the possible failure of a device. In addition, machine learning is now beginning to be applied to CbM, correlating monitoring data with other sensing data to infer more likely conditions for the device.

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