perform its task, but it should be observed more frequently and may
`likely require additional maintenance.
`Sometimes, these three zones of operation (normal, warning, critical)
`can correlate to the three stages of a machine’s maintenance cycle:
`early life, midlife, and end-of-life, in which case they may influence
`the vibration monitoring strategy. For example, during early life, an
`instrument might only require daily, weekly, or monthly observation
`of key vibration attributes. As it moves into midlife, this might change
`to hourly observation, and as it approaches end-of-life, vibration
`monitoring might occur even more often, especially in cases where
`people or the asset are at risk. At this stage, machines that monitor
`vibration using portable equipment will accumulate recurring
`costs that might become prohibitive when compared to the cost of
`maintenance. While major assets can justify special attention, many
`other instruments cannot bear the recurring cost. To complement
`manual measurements, embedded MEMS-based sensors provide a
`more cost-effective approach for equipment that requires real-time
`vibration data.
`
`EARLY LIFE
`
`MIDLIFE
`
`END-OF-LIFE
`
`CRITICAL
`
`WARNING
`
`NORMAL
`
`VIBRATION
`
`
`
`TIME
`
`Figure 1. Example vibration vs. time.
`
`Nature of Vibration
`Vibration is a repetitive mechanical motion. A number of attributes
`are important for developing a vibration-sensing instrument.
`First, an oscillating motion often has both linear and rotational
`components. Most vibration-sensing relationships tend to focus on
`the magnitude of the oscillation, not on absolute position tracking,
`so linear sensors such as MEMS accelerometers are sufficient for
`capturing motion information. When the motion is mostly linear,
`understanding the direction can be important, especially when
`using single-axis sensors. Conversely, a 3-axis sensor can offer more
`mounting flexibility, as the orthogonal orientation enables pickup
`on one or more axes regardless of the vibration direction.
`Since vibration is periodic, spectral analysis offers a convenient
`way to characterize the vibration profile (the relationship between
`vibration magnitude and frequency). The profile shown in Figure
`2 has both broadband and narrow-band components, with primary
`vibration at ~1350 Hz, four harmonics, and some low-level wideband
`content. Every piece of moving equipment will have its own vibration
`profile, with the narrow-band response often representing the
`natural frequencies of the equipment.
`
`An Introduction to MEMS
`Vibration Monitoring
`
`By Mark Looney
`
`Introduction
`Inertial MEMS sensors play a significant role in the massive expansion
`of today’s personal electronic devices. Their small size, low power, ease
`of integration, high level of functionality, and superb performance
`encourage and enable innovation in gadgets such as smartphones,
`gaming controllers, activity trackers, and digital picture frames.
`In addition, inertial MEMS sensors have substantially improved
`reliability and reduced cost in automotive safety systems, allowing
`them to be deployed in most automobiles.
`The continuous advancement in functional integration and
`performance has also helped MEMS accelerometers and gyroscopes
`find their way into many different industrial systems. Some of these
`applications offer lower-cost alternatives to present products and
`services, while others are integrating inertial sensing for the very first
`time. Vibration monitoring is emerging as an application that has
`both types of users. Traditional instruments that monitor machine
`health for maintenance and safety often use piezoelectric technology.
`High-speed automation equipment monitors vibration to trigger
`feedback control of lubrication, speed, or belt tension—or to shut
`down equipment for quick attention from maintenance staff.
`Although piezoelectric devices have a mature user base, MEMS
`accelerometers offer easy integration and reduced cost to an emerging
`group of new users. In addition, their advanced functional integration
`allows devices such as the ADIS16229 digital MEMS vibration
`sensor with embedded RF transceiver to provide a complete solution
`including signal processing and communications. This type of
`programmable device can wake itself up periodically, capture time-
`domain vibration data, perform a fast Fourier transform (FFT) on
`the data record, apply user-configurable spectral analysis on the
`FFT result, offer simple pass/fail results over an efficient wireless
`transmission, provide access to data and results, and then go back
`to sleep. New adopters of vibration sensing are finding that quick
`deployment and the reasonable cost of ownership are good reasons
`to evaluate fully integrated MEMS devices.
`
`Vibration Monitoring Applications
`When using vibration to observe machine health, the objective is to
`correlate observable vibration with typical wear-out mechanisms,
`such as bearings, gears, chains, belts, brushes, shafts, coils, and
`valves. In a typical machine, at least one these mechanisms requires
`regular maintenance. Figure 1 shows three examples of the vibration
`vs. time relationship for a normal wear-out mechanism. Although
`it takes time and experience to develop this type of relationship, a
`well-correlated vibration signature can be a cost-saving alternative
`to regular maintenance that follows short cycle times. Using actual
`observations, such as vibration, provides an opportunity to take
`quick action when warning conditions are detected (red curve), while
`avoiding premature maintenance on machines that have more life
`remaining (blue and green curves).
`Figure 1 also shows two alarm settings (warning, critical) and three
`stages of the machine’s maintenance cycle (early, middle, end). The
`warning level defines the maximum vibration during normal operation,
`where the observed vibration contains no indication of potential
`hazard for the machine or support staff. When in the normal range,
`some instruments can support infrequent vibration measurements.
`The critical level indicates that the asset is at risk of severe damage,
`creating unsafe conditions for support staff or the environment.
`Obviously, machine operators want to avoid operation at this level,
`and typically take the machine off line. When the vibration is above
`the warning level, but below the critical level, the machine can still
`Figure 2. Example vibration profile, natural frequency ~1350 Hz.
`Page 1 of 3
`www.analog.com/analogdialogue
`
`PRIMARY/NATURAL FREQUENCY
`
`HARMONICS
`
`SPREAD SPECTRUM
`CONTENT
`
`0
`
`1k
`
`2k
`
`3k
`
`4k
`5k
`6k
`FREQUENCY (Hz)
`
`7k
`
`8k
`
`9k
`
`10k
`
`25
`
`20
`
`15
`
`10
`
`5
`
`0
`
`VIBRATION (mg)
`
`
`
`Analog Dialogue 48-06, June (2014)
`
`HAPTIC EX2013
`1
`
`

`

`Signal Processing
`The sensor selection and signal-processing architecture depends
`on the application’s objectives. As an example, the signal chain
`shown in Figure 3 continuously monitors a specific frequency band,
`providing warning and critical lights on a nearby control panel. The
`manufacturer’s insight into the mechanical design aids with the band-
`pass filter design, specifically with the start frequency, stop frequency,
`and pass-band roll-off rates. Rotation speed, natural frequencies of
`the mechanical structure, and fault-specific vibration behaviors can
`all influence the band-pass filter. While this type of approach is simple,
`vibration monitoring requirements can change as historical data from
`a particular machine becomes available. Changes in monitoring
`requirements can lead to changes in the filter structure, which can
`present a recurring engineering cost. Developers can trade complexity
`for flexibility by digitizing the sensor response, implementing key
`signal processing functions such as filter, rms computation, and level
`detectors, and leveraging auxiliary I/O outputs to control indicator
`lights or provide a numerical output.
`
`MEMS
`ACCELEROMETER
`
`BAND-
`PASS
`FILTER
`
`RMS
`TO
`DC
`
`WARNING
`LEVEL
`DETECTOR
`
`CRITICAL
`LEVEL
`DETECTOR
`
`YELLOW
`LIGHT
`
`RED
`LIGHT
`
`
`
`Figure 3. Time-domain vibration signal chain example.
`
`Figure 4 shows a signal chain for the ADIS16228, which uses a
`digital triaxial vibration sensor with FFT analysis and storage to
`monitor the spectral content of equipment vibration.
`
`
`
`LOW-
`PASS
`FILTER
`
`MEMS
`ACCELEROMETER
`
`ADC
`
`TIME
`DATA
`RECORD
`CAPTURE
`
`WINDOW
`
`FFT
`
`SPECTRAL
`ALARMS
`
`RECORD
`STORAGE
`
`Figure 4. ADIS16228 signal chain for spectral vibration analysis.
`
`Core Sensor
`The core sensor for either approach can be a MEMS accelerometer.
`The most important attributes for selecting a core sensor will be
`the number of axes, package/assembly requirements, electrical
`interface (analog/digital), frequency response (bandwidth),
`measurement range, noise, and linearity. While many triaxial MEMS
`accelerometers support direct connection with most embedded
`processors, capturing the best available level of performance might
`require single- or dual-axis solutions that have analog outputs. For
`example, the ADXL001 high-performance wideband iMEMS®
`accelerometer leverages its 22-kHz resonance to provide one of the
`widest available bandwidths, but it is only available as a single-axis,
`analog-output device. Analog outputs can enable a quick interface
`in systems that have an available analog-to-digital channel, but the
`present trend of development seems to favor those sensors that have
`digital interfaces.
`The core sensor’s frequency response and measurement range
`determine the maximum vibration frequency and amplitude that it
`can support before saturating the output. Saturation degrades the
`spectral response, creating spurious content that can cause false
`alarms, even when the saturation frequency does not interfere with
`
`a frequency of interest. The measurement range and frequency
`response are related by
`
`A
`
`=p-p
`
`D
`p-p
`
`×
`2
`
`where D is the physical displacement, ω is the vibration frequency,
`and A is the acceleration.
`While the frequency response and measurement range place upper
`boundaries on the sensor’s response, its noise and linearity place
`limits on the resolution. The noise will establish the lower limit of the
`vibration that will cause a response in the output, while the linearity
`will determine how much false harmonic content is generated by a
`vibration signal.
`
`Analog Filter
`The analog filter limits the signal content to one Nyquist zone, which
`represents one half of the sample rate in the example system. Even
`when the filter cut-off frequency is within the Nyquist zone, it is
`impossible to have infinite rejection of higher-frequency components,
`which can still fold back into the pass band. For a system monitoring
`only the first Nyquist zone, this fold-back behavior can create false
`failures and distort the view of the vibration content at a particular
`frequency.
`
`Windowing
`Time-coherent sampling is often not practical in vibration-sensing
`applications, as nonzero sample values at the start and end of the time
`record result in large spectral leakage, which can degrade the FFT
`resolution. Applying a window function before calculating the FFT
`can help manage the spectral leakage. The best window function is
`dependent on the actual signal, but in general, the trade-offs include
`process loss, spectral leakage, lobe location, and lobe levels.
`
`Fast Fourier Transform (FFT)
`The FFT is an efficient algorithm for analyzing discrete time
`data. The process transforms a time record into a discrete spectral
`record, where each sample represents a discrete frequency
`segment of the Nyquist zone. The total number of output samples
`is equal to the number of samples in the original time record,
`which in most cases represents a number in the binomial series
`(1, 2, 4, 8 … ). Spectral data has both magnitude and phase information,
`which can be represented in either rectangular or polar form. When
`in rectangular form, one half of the FFT bins contain magnitude
`information, while the other half contains phase information. When
`in polar form, one half of the FFT bins contain the real result, while
`the other half contains the imaginary result.
`In some cases, both magnitude and phase information are helpful,
`but the magnitude/frequency relationship often contains enough
`information for detecting key changes. For devices that offer only
`magnitude results, the number of FFT bins is equal to one half of
`the samples in the original time-domain record. The FFT bin width
`equals the sample rate divided by the total number of records. In a
`way, each FFT bin is like an individual band-pass filter in the time
`domain. Figure 5 provides an example of an actual MEMS vibration
`sensor, which samples at 20480 samples per second (SPS) and starts
`with 512-point records. In this case, the sensor only provides the
`magnitude information, so the total number of bins is 256 and the
`bin width is equal to 40 Hz (20480/512).
`
`FFT MAGNITUDE, BIN #1
`
`fBW
`
`0
`
`1
`
`2
`
`3
`
`253
`
`254
`
`255
`
`
`
`0
`
`RESOLUTION
`NOISE FLOOR
`
`fS/2
`
`Figure 5. ADIS16228 FFT output.
`
`2
`
`Analog Dialogue 48-06, June (2014)
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`Page 2 of 3
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`

`

`parameters such as power supply, temperature, date, time, sample
`rate, alarm settings, and filtering.
`
`Interface
`The interface depends on the existing infrastructure in a particular
`plant. In some cases, industrial cable-ready communication standards
`such as Ethernet or RS-485 are readily available, so the interface
`between a smart sensor and the communication system might be an
`embedded processor. In other cases, that same embedded processor
`might be used to interface the smart sensor with an existing wireless
`protocol, such as Wi-Fi, ZigBee, or a system-specific standard. Some
`smart sensors, such as the ADIS16000 wireless gateway node for
`remote sensors and the ADIS16229, come with a ready-to-deploy
`wireless interface that is available through common embedded
`interfaces such as SPI or I2C.
`
`Conclusion
`Inertial MEMS technology is ushering in a new era of vibration
`monitoring and is enabling a wider user base for this type of
`instrumentation. Performance, packaging, and familiarity may
`contribute to continued use of piezoelectric technology, but vibration
`monitoring is clearly growing and evolving. Through functional
`integration and ease of adoption, MEMS devices are gaining
`increasing attention in new vibration monitoring applications.
`Convenience, through advanced signal processing at the point of
`sensing, reduces the monitoring burden to a simple state (normal,
`warning, critical) for most situations. In addition, remote data
`access through convenient communication channels is creating new
`applications for vibration monitoring instruments. Future advances
`in key performance metrics (noise, bandwidth, and dynamic range)
`and the high level of functional integration will help this trend to
`continue in the near future.
`
`References
`Circuit Note CN0303. MEMS-Based Vibration Analyzer with
`Frequency Response Compensation.
`Scannell, Bob. MS-2507. Enabling Continuous and Reliable Process
`Monitoring with Wireless Vibration Sensors.
`
`Author
`Mark Looney [mark.looney@analog.com] is
`an iSensor® applications engineer at Analog
`Devices in Greensboro, North Carolina. Since
`joining ADI in 1998, he has accumulated
`experience in sensor-signal processing, high-
`speed analog-to-digital converters, and dc-to-dc
`power conversion. He earned a B.S (1994) and
`M.S (1995) degree in electrical engineering
`from the University of Nevada, Reno, and
`has published several articles. Prior to joining ADI, he helped start
`IMATS, a vehicle electronics and traffic-solutions company, and
`worked as a design engineer for Interpoint Corporation.
`
`The bin width is important because it establishes the frequency
`resolution as the frequency shift from one bin to an adjacent bin,
`and because it determines the total noise the bin will contain.
`The total noise (rms) is equal to the product of the noise density
`(~240 μg/√Hz) and the square root of the bin width (√40 Hz),
`or ~1.5 mg rms. For low-frequency applications, where noise tends to
`have the most influence on resolving vibration, a decimation filter prior
`to the FFT process can help improve the frequency and magnitude
`resolution without requiring a change in the ADC’s sample frequency.
`Decimating the 20480 SPS sample rate by a factor of 256 enhances
`the frequency resolution by a factor of 256 while reducing the noise
`by a factor of 16.
`
`Spectral Alarms
`One of the key advantages of using an FFT is that it enables simple
`application of spectral alarms. Figure 6 provides an example that
`includes five independent spectral alarms that monitor the natural
`frequency in the machine (#1), its harmonics (#2, #3, and #4), and
`the wideband content (#5). The warning and critical levels correspond
`to the levels in the machine-health vibration vs. time profile. The
`start and stop frequencies complete the process variable definition
`represented by this relationship. When using an embedded processor,
`the spectral alarm definition variables (start/stop frequencies, warning/
`critical alarm levels) can be in configurable register locations that use
`digital codes for configuration. Using the same scale factors and bin
`numbering scheme can greatly simplify this process.
`
`WARNING ALARM
`EXAMPLE
`
`CRITICAL ALARM
`EXAMPLE
`
`START
`FREQUENCY
`
`STOP
`FREQUENCY
`
`1
`
`2
`
`3
`
`4
`
`5
`
`0
`
`1k
`
`2k
`
`3k
`
`4k
`5k
`6k
`FREQUENCY (Hz)
`
`7k
`
`8k
`
`9k
`
`10k
`
`25
`
`20
`
`15
`
`10
`
`5
`
`0
`
`VIBRATION (mg)
`
`
`
`Figure 6. Example FFT with spectral alarms.
`
`Record Management
`One of the key functions associated with process variable relationships
`is record management. Storing FFT records from different stages
`of each machine’s lifetime enables analysis of a variety of behaviors
`that may lead to a wear-out curve that contributes to maintenance
`and safety planning. In addition to compiling historical vibration
`data, some will find value in capturing condition data associated with
`
`Analog Dialogue 48-06, June (2014)
`
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`Page 3 of 3
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