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Choosing the Most Suitable Predictive Maintenance Sensor Chris Murphy

(Source: LeoWolfert/Shutterstock.com)


Condition-based monitoring (CbM) involves monitoring machines or assets using sensors to measure the current state of health. Predictive maintenance (PdM) consists of techniques, such as CbM, machine learning, and analytics, to predict upcoming machine or asset failures. When monitoring the machine’s health, it is critically important to select the most suitable sensors to ensure faults can be detected, diagnosed, and even predicted.

The best PdM strategy is one that efficiently utilizes as many techniques and sensors as possible to detect faults early, and to a high degree of confidence, so there is no one-sensor-fits-all solution. Here, we’ll seek to clarify why predictive maintenance sensors are vital to the early detection of faults in PdM applications, and their strengths and weaknesses.

System Fault Timeline

Figure 1 shows a simulated timeline of events from installing a new motor to motor failure along with the recommended predictive maintenance sensor type. When a new motor is installed, it is under warranty. After several years, the warranty will expire, and it is at this point that a more frequent manual inspection regiment will be implemented.

Figure 1: The Machine health vs. time graph shows the expected time a fault would be detected as a motor moves through the warranty cycle. (Source: Analog Devices)

If a fault emerges in between these scheduled maintenance checks, there is a likelihood of unplanned downtime. In this case, what becomes vitally important is having the right predictive maintenance sensor to detect potential faults as early as possible. For this reason, we will focus on vibration and acoustic sensors. Vibration analysis is generally perceived as the best starting point for PdM.

Sensor and System Fault Considerations

More than 90 percent of rotating machinery in industrial and commercial applications use rolling-element bearings. The distribution of failed components of a motor indicates that it is crucial to focus on bearing monitoring when selecting a PdM sensor. To detect, diagnose, and predict potential faults, a vibration sensor must have low noise and wide bandwidth capabilities.

Some of the most common faults associated with rotating machines and some corresponding vibration sensor requirements for use in PdM applications are shown in Table 1. To detect faults as early as possible, PdM systems typically require high-performance sensors. The performance level of the predictive maintenance sensor used on an asset is correlated to the importance of assets being continuously able to operate reliably in the overall process and the asset’s cost.

Table 1: Brief Overview of Machine Fault and Vibration Sensor Considerations (Source: Analog Devices)

Sensors for PdM

A micro-electromechanical system (MEMS) ultrasonic microphone analysis enables the monitoring of motor health in complicated assets, in the presence of increased audible noise, because it listens to sounds in the non-audible spectrum (20kHz to 100kHz) where there is far less noise. The wavelengths of low-frequency audible signals typically range from approximately 17mm to 17m long. The wavelengths of high-frequency signals range from about 3mm to 16mm long. When the frequency of the wavelength increases, the energy increases, making the ultrasound more directive. This is extremely useful when trying to pinpoint a failure in a bearing or housing.

Accelerometers are the most commonly used vibration sensor, and vibration analysis is the most widely employed PdM technique, mainly used on large rotating equipment such as turbines, pumps, motors, and gearboxes. Table 2 shows some of the critical specifications for consideration when selecting high-performance MEMS vibration and acoustic sensors versus the gold standard piezo vibration sensor.

Table 2: Predictive Maintenance Sensor Performance Specifications

*MEMS accelerometer modules can cost more than $30, but they are full-system solutions, whereas all other parts referenced are sensors only.
**Key: Worst, Medium, Best

Although it is difficult to recommend a single vibration sensor for use in a PdM system, accelerometers have a successful history and continue to evolve and improve. Analog Devices offers a range of MEMS accelerometers from general-purpose, low power, low noise, high stability, and high g, as well as intelligent edge-node modules shown in Figure 2. The ADcmXL3021 Triaxial Vibration Sensor is one excellent example of a dedicated PdM module solution. Analog Devices was first to market with a family of PdM-capable MEMS accelerometers (20kHz+ bandwidth, 25μg/√Hz noise density) and remains the only MEMS accelerometer supplier with these performance levels.

Figure 2: Three-axis MEMS CbM module with integrated ADC, processor, FFT, and statistics, as well as a mechanical package with resonant frequency over 50kHz. (Source: Analog Devices)

When choosing the most suitable vibration sensor for your PdM solution, the real challenge lies in pairing sensors to meet the most likely potential failure modes of your assets. MEMS microphones are not yet proven to be robust enough to reliably detect all vibration-based failure modes in the harshest of environments. In contrast, accelerometers, the industry standard for vibration sensing, have been implemented successfully and performed reliably for decades. MEMS ultrasonic microphones have shown promising performance in detecting bearing faults earlier than accelerometers, and this potential symbiotic relationship could deliver the best PdM solution for your asset’s vibration analysis needs in the future.

The Choosing the Most Suitable Predictive Maintenance Sensor blog was written by Chris Murphy and originally published on www.analog.com. Chris Murphy and Paul Golata revised the blog for mouser.com.

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Chris MurphyChris Murphy is an applications engineer with the European Centralized Applications Center, based in Dublin, Ireland. He has worked for Analog Devices since 2012, providing design support on motor control and industrial automation products. He has an M.Eng. in electronics by research and a B.Eng. in computer engineering. He can be reached at christopher.murphy@analog.com.


Paul GolataAs a Technology Specialist, Mr. Golata is accountable for driving the strategic leadership, tactical execution, and overall product line and marketing direction for solid-state lighting and other advanced technology products.  Prior to Mouser Electronics, he served in various Marketing and Sales roles for various high technology companies.  Mr. Golata holds a BSEET (DeVry) and MBA (Pepperdine).

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