“Sensors have always played an important role as “organs” in Electronic devices to obtain external information, and MEMS accelerometers are essential to measure information such as acceleration, tilt, vibration or shock – and are suitable for use in wearable fitness devices. To the wide application of industrial platform stabilization system.
How to choose a MEMS accelerometer?
Sensors have always played an important role as “organs” in electronic devices to obtain external information, and MEMS accelerometers are essential to measure information such as acceleration, tilt, vibration or shock – and are suitable for use in wearable fitness devices. To the wide application of industrial platform stabilization system.
There are hundreds of accelerometer devices on the market to choose from, varying in cost and performance, and requiring different accelerometers for different applications. How to familiarize yourself with the key parameters and characteristics of MEMS accelerometers and choose the most suitable one for wearables, structural health monitoring (SHM), asset health monitoring (AHM), vital sign monitoring (VSM) and IoT applications quickly and well Accelerometer, welcome to read this article to learn.
Familiar with MEMS performance
There is no industry standard that defines what category an accelerometer falls into. The general classification of accelerometers and their corresponding applications are shown in Table 1. The bandwidth and g-value ranges shown are typical for accelerometers used in the end applications listed.
Table 1. Accelerometer Classes and Typical Application Areas
Figure 1 shows a snapshot of various MEMS accelerometers, grouping each sensor according to its application-specific key performance metrics and level of intelligence/integration. An important focus of this paper is a new generation of accelerometers based on enhanced MEMS structures and signal processing, and world-class packaging technology, with stability and noise performance comparable to more expensive specialized devices, while consuming less power. These characteristics and other key specifications of the accelerometer are discussed in detail below, depending on the application.
Figure 1. Application layout of ADI’s select MEMS accelerometers
Main criteria: bias stability, offset temperature drift, low noise, repeatability, vibration correction, cross-axis sensitivity.
Accurate tilt detection is a demanding application for MEMS capacitive accelerometers, especially in the presence of vibration. Achieving a tilt accuracy of 0.1° with a MEMS capacitive accelerometer in a dynamic environment is very difficult – 1° is easier to achieve. In order for an accelerometer to measure tilt effectively, a good understanding of the sensor performance and the end-use environment is necessary. A static environment is more favorable for tilt measurement than a dynamic environment, because vibration or shock may corrupt the tilt data and cause serious measurement errors. The most important characteristics of tilt measurement are temperature coefficient offset, hysteresis, low noise, short/long term stability, repeatability and good vibration correction.
0g bias accuracy, 0g bias drift caused by soldering, 0g bias drift caused by PCB housing alignment, 0g bias temperature coefficient, sensitivity accuracy and temperature coefficient, nonlinearity, and cross-axis sensitivity errors are observable , and can be reduced through a post-assembly calibration process. Hysteresis, 0g bias drift over life, sensitivity drift over life, 0g drift due to moisture, and PCB bending and twisting due to temperature changes over time, etc. These error terms cannot be addressed by calibration or other methods and require It can be reduced with some level of in-situ maintenance.
ADI’s accelerometers can be divided into MEMS (ADXLxxx) and iSensor® (ADIS16xxx) special purpose devices. iSensors or smart sensors are highly integrated (4 to 10 degrees of freedom) and programmable devices for complex applications in dynamic environments. These highly integrated plug-and-play solutions include comprehensive factory calibration, embedded compensation, and signal processing, addressing many of the aforementioned errors requiring in-situ repair, greatly reducing the design and verification burden. This comprehensive factory calibration provides sensitivity and bias characteristics over the specified temperature range (typically −40°C to +85°C) for the entire sensor signal chain. Therefore, each iSensor device has its own unique compensation formula that produces accurate measurements when installed. For some systems, factory calibration can eliminate system-level calibration, greatly simplifying operation.
iSensor devices are developed specifically for certain applications.For example, the ADIS16210 shown in Figure 2 is designed and customized for tilt applications, so it provides
Figure 2. ADIS16210 Precision Triaxial Tilt
Next-generation accelerometer architectures such as the ADXL355 offer more functionality (tilt, condition monitoring, structural health, IMU/AHRS applications) and contain fewer application-specific but feature-rich integrated blocks, as shown in Figure 3.
Figure 3. ADXL355 Low Noise, Low Drift, Low Power 3-Axis MEMS Accelerometer
The following compares the ADXL345, a general-purpose accelerometer, and the ADXL355, a new generation of low-noise, low-drift, low-power accelerometers ideal for a wide range of applications, such as IoT sensor nodes and inclinometers. This comparison looks at the sources of error in tilt applications, and the errors that can be compensated or eliminated. Table 2 lists the ideal performance specifications for the consumer ADXL345 accelerometer and estimates of the corresponding tilt error. When trying to achieve the best tilt accuracy, some form of temperature stabilization or compensation must be employed. In the example below, a constant temperature of 25°C is assumed. The most important error contributors that cannot be fully compensated are temperature drift offset, offset drift, and noise. The bandwidth can be reduced to reduce noise, as tilt applications typically require less than 1kHz bandwidth.
Table 2. ADXL345 Error Source Estimates
Table 3 lists the same standards that apply to the ADXL355. The short-term bias value is estimated from the Allan variance plot in the ADXL355 data sheet. At 25°C, the general-purpose ADXL345 has an estimated tilt accuracy of 0.1° after compensation. The industrial grade ADXL355 has an estimated tilt accuracy of 0.005°. Comparing the ADXL345 and ADXL355 shows that the errors caused by major error contributors have been significantly reduced, such as the error caused by noise from 0.05° to 0.0045°, and the error caused by offset drift from 0.057° to 0.00057°. This shows that MEMS capacitive accelerometers have made a huge leap in performance such as noise and bias drift, and are able to provide a higher level of tilt accuracy under dynamic conditions.
Table 3. ADXL355 Error Source Estimates
Choosing a higher-grade accelerometer is critical to achieving the desired performance, especially when the application requires tilt accuracy of less than 1°. Application accuracy depends on application conditions (large temperature fluctuations, vibration) and sensor selection (consumer vs industrial or tactical). In this case, the ADXL345 would require extensive compensation and calibration work to achieve a tilt accuracy of less than 1°, adding effort and cost to the overall system. Depending on the amount of vibration in the final ambient and temperature range, it may not even be possible to achieve the above accuracy. The temperature coefficient offset drift of 1.375° over the 25°C to 85°C range exceeds the requirement for less than 1° of tilt accuracy.
The ADXL355 has a maximum temperature coefficient offset drift of 0.5° from 25°C to 85°C.
ADXL354 and ADXL355 repeatability (±3.5mg/0.2° on X and Y axis, ±9mg/0.5° on Z axis) is a 10-year life prediction including High Temperature Operating Life Test (HTOL) (TA=150°C, VSUPPLY=3.6V, 1000 hours), temperature cycling (−55°C to +125°C and 1000 cycles), velocity random walk, wideband noise, and temperature hysteresis induced offsets. These new accelerometers provide repeatable tilt measurements under all conditions, achieve minimal tilt errors in harsh environments without extensive calibration, and minimize the need for post-deployment calibration. The ADXL354 and ADXL355 accelerometers are temperature stable with a zero offset factor of 0.15mg/°C (max). This stability minimizes the resource and cost overhead associated with calibration and testing, enabling device OEMs to achieve higher throughput rates. In addition, the product comes in a hermetically sealed package, which ensures that the repeatability and stability of the final product will always meet its specifications after leaving the factory.
Often, repeatability and rejection of vibration correction error (VRE) are not shown on data sheets because these parameters may expose lower product performance. For example, the ADXL345 is a general-purpose accelerometer for consumer applications where VRE is not an important parameter of concern for the designer. However, in demanding applications such as inertial navigation, tilting applications, or specific environments with high vibration, VRE rejection can be a major concern for designers, so the ADXL354/ADXL355 and ADXL356/ADXL357 data sheets give such parameters .
As shown in Table 4, VRE is the offset error introduced when the accelerometer is exposed to broadband vibration. When the accelerometer is exposed to vibration, VRE can cause significant errors in tilt measurements compared to the 0g offset due to temperature drift and noise. This is one of the main reasons why data sheets are no longer used, as it is easy to obscure other major specs.
VRE is the accelerometer’s response to AC vibration (rectified to DC). The vibration of these DC rectifications can shift the accelerometer offset, causing serious errors, especially in tilting applications where the signal of interest is a DC output. Any small change in DC offset can be interpreted as a change in tilt, resulting in a system-level error.
Table 4. Errors in Slope
*The range of 1g azimuth misalignment caused by 2.5grms vibration is ±2g.
Various resonances and filters in accelerometers (ADXL355 in this case) can cause VRE because VRE is strongly frequency dependent. These resonances amplify vibrations by a factor equal to the Q-factor of the resonances, and dampen vibrations at higher frequencies due to the second-order dipole response of the resonator. The higher the resonance quality factor of the sensor, the greater the vibration amplitude and the greater its VRE. A larger measurement bandwidth will include vibrations in the high frequency band, resulting in a higher VRE, as shown in Figure 4. Many vibration-related problems can be avoided by choosing the appropriate bandwidth for the accelerometer to suppress high-frequency vibrations.
Figure 4. ADXL355VRE Tests at Different Bandwidths
Static tilt measurements typically require a low-g accelerometer of ±1g to ±2g with a bandwidth of less than 1.5kHz. The analog output ADXL354 and digital output ADXL355 are low noise density (20μg√Hz and 25μg√Hz respectively), low 0g offset drift, low power consumption triaxial accelerometer, integrated temperature sensor, optional measurement range, as shown in Table 5 Show.
Table 5. ADXL354/ADXL355/ADXL356/ADXL357 Measurement Ranges
The ADXL354/ADXL355 and ADXL356/ADXL357 are available in a hermetic package that contributes to excellent long-term stability. Performance improvement and packaging are usually positively correlated, as shown in Figure 5. Packaging is often overlooked when manufacturers can use packaging to achieve better settling and drift performance. This is a key area of focus for ADI, and we offer a wide range of sensor packages to suit different application areas.
High temperature and dynamic environment
Before accelerometers suitable for high temperatures or harsh environments were available, some designers had to use standard temperature ICs in situations far beyond data sheet limits. This means that the end user bears the responsibility and risk of verifying device quality at high temperatures, which is expensive and time-consuming. It is a well-known fact that hermetic packages can withstand high temperatures, preventing corrosion by providing a barrier against moisture and contamination. ADI offers a variety of hermetically sealed devices with enhanced temperature stability and performance. ADI has also vigorously studied the performance of plastic packages at high temperatures, especially the ability of the lead frame and leads to adapt to high temperature soldering processes, making them robust and reliable in high shock and vibration environments. Therefore, ADI offers 18 accelerometers rated from −40°C to +125°C, including the ADXL206, ADXL354/ADXL355/ADXL356/ADXL357, ADXL1001/ADXL1002, ADIS16227/ADIS16228, and ADIS16209. Most competitors do not offer MEMS capacitive accelerometers that can operate in the −40°C to +125°C temperature range or in harsh environmental conditions such as heavy industrial machinery and downhole drilling.
Figure 5. Example of performance gains due to advanced packaging techniques and calibration
Inclination measurements in harsh environments with temperatures over 125°C are extremely challenging. The ADXL206 is a high precision (tilt precision
Inclination measurements in dynamic environments with vibration, such as agricultural equipment or drones, require an accelerometer with a higher g range, such as the ADXL356/ADXL357. Accelerometer measurements with a limited g range may clip, resulting in increased output offset. Clipping can be caused by the sensitive axis being in a 1g gravity field, or by a shock that has a fast rise time but slow decay. The higher g range reduces accelerometer clipping, which reduces offset and provides better tilt accuracy in dynamic applications.
Figure 6 shows a limited g range measurement for the ADXL356 Z axis, where 1g already exists. Figure 7 shows the same measurement, but with an extended g range from ±10g to ±40g. It can be clearly seen that the extended g range of the accelerometer significantly reduces the offset due to clipping.
The ADXL354/ADXL355 and ADXL356/ADXL357 offer excellent vibration correction, long-term repeatability, and low noise performance in a small footprint, making them ideal for tilt detection applications in static and dynamic environments.
Figure 6. ADXL356 VRE, Z-axis offset relative to 1g, ±10g range, Z-axis direction = 1g
Figure 7. ADXL356 VRE, Z-axis offset relative to 1g, ±40g range, Z-axis direction = 1g
Main criteria: noise density, velocity random walk, bias stability in motion, bias repeatability, and bandwidth.
Detecting and understanding motion can benefit many applications. It can be beneficial to gain control over the movements that occur in a system, and then use that information to improve performance (reduce response time, improve accuracy, speed up operation), enhance safety or reliability (system shuts down in dangerous situations), or obtain other value-added features of. Due to the complexity of motion, there are a number of stabilization applications that require a combination of gyroscopes and accelerometers (sensor fusion, as shown in Figure 8), such as UAV surveillance equipment and onboard antenna pointing systems.
Figure 8.6 Degree of Freedom IMU
A DOF IMU uses multiple sensors in order to compensate for each other’s weaknesses. What looks like simple inertial motion in one or two axes may actually require accelerometer and gyroscope sensor fusion to remove vibration, gravity, and other contributing factors that cannot be accurately measured by accelerometers or gyroscopes alone. Accelerometer data includes gravity components and motion acceleration. The two are indistinguishable, but a gyroscope can be used to remove the gravity component from the accelerometer output. In order to determine position from acceleration, integration is required, after which the error caused by the gravitational component of the accelerometer data can grow rapidly. The gyroscope alone is not enough to determine the position due to accumulated errors. Gyroscopes do not detect gravity, so they can be used as secondary sensors to accelerometers.
In stability applications, MEMS sensors must accurately measure platform orientation, especially during motion. Figure 9 is a block diagram of a typical platform stabilization system using servo motors to correct angular motion. The feedback/servo motor controller converts the orientation sensor data into corrective control signals for the servo motors.
Figure 9. Basic platform stabilization system
The final application will determine the level of accuracy required, and the quality of the sensor chosen (consumer or industrial) will determine whether it can be achieved. It is important to distinguish between consumer-grade and industrial-grade devices, and sometimes the distinction is subtle and may require careful consideration. Table 6 shows the main differences between consumer-grade accelerometers and mid-range industrial-grade accelerometers integrated in the IMU.
Table 6. Industrial MEMS devices comprehensively measure all known potential error sources with an accuracy level more than an order of magnitude better than consumer-grade devices
In some cases where conditions are favorable and lower precision data is acceptable, performance needs can be met using lower precision devices. However, the demand for sensors that can operate in dynamic environments is growing rapidly, and lower precision devices are greatly affected by the inability to reduce vibration effects or temperature effects in actual measurements, and it is difficult to achieve pointing accuracy of less than 3° to 5°. Most low-end consumer devices do not provide parameters such as vibration correction, angular random walk, etc., which can be the largest source of error in industrial applications.
To achieve pointing accuracy of 1° or even 0.1° in dynamic environments, the designer’s device selection must focus on the sensor’s ability to suppress the effects of temperature drift errors and vibration. Sensor filtering and algorithms (sensor fusion), while key elements to improve performance, cannot close the gap between consumer-grade and industrial-grade sensors. The performance of ADI’s new industrial IMU is close to that used in previous generation missile guidance systems. Devices such as the ADIS1646x and the announced ADIS1647x provide precision motion detection in standard and mini IMU form factors, breaking into specialized application areas of the past.
Choosing the Right MEMS
Choosing the most suitable accelerometer for an application can be difficult because data sheets from different manufacturers can vary widely, making it difficult to determine what the most important specifications are. In the second part of this article, we will focus on key technical indicators and characteristics from the perspective of wearable devices, condition monitoring and IoT applications.
Key metrics: Low power consumption, small size, integrated features designed to enhance power saving performance, and usability.
A key specification for accelerometers for battery-operated wearable applications is ultra-low power consumption (typically in the μA range) to ensure maximum battery life. Other key metrics are size and integrated features, such as alternate ADC channels and deep FIFOs, which serve to enhance power management and functionality in the end application. For these reasons, MEMS accelerometers are often used in wearable applications. Table 1 shows some of the vital signs monitoring (VSM) applications and their corresponding settings in specific applications. Accelerometers used in wearable applications can often classify motion; detect free fall; measure the presence of motion to determine whether to power up, shut down, or sleep the system; and aid in data fusion for ECG and other VSM measurements. The same accelerometers are also used in wireless sensor networks and IoT applications because of their ultra-low power consumption.
Table 7. Motion Detection Requirements for VSM Wearable Applications
When selecting an accelerometer for an ultra-low power application, it is essential to observe the sensor’s functionality at the power consumption levels stated in the data sheet. A key metric to watch is whether the bandwidth and sampling rate drop to a level where it is impossible to measure usable acceleration data. Some competing products maintain low power consumption by shutting down and waking up every second, but doing so misses critical acceleration data because the effective sampling rate is reduced. In order to measure the range of real-time human motion, a significant increase in power consumption is required. The ADXL362 and ADXL363 do not alias the input signal by undersampling; they sample the full bandwidth of the sensor at the full data rate. Power consumption varies dynamically with the sampling rate, as shown in Figure 10. It is important to note that these devices can sample at rates up to 400Hz while consuming only 3µA. In wearables, these higher data rates enable additional functionality such as single/double-tap detection. The sampling rate can be reduced to 6Hz so that the device can start up when picked up or when motion is detected, with an average power consumption of 270nA. This also makes the ADXL362 and ADXL363 ideal for implantable applications where battery replacement is very difficult.
Figure 10. ADXL362 supply current versus output data rate.
In some applications, the accelerometer only needs to be polled for acceleration once or several times per second. For such applications, the ADXL362 and ADXL363 offer a wake-up mode that consumes only 270nA. The ADXL363 integrates a triaxial MEMS accelerometer, a temperature sensor (0.065°C typical scale factor), and an onboard ADC input (for synchronously converting external signals) in a small, low profile (3mmx3.25mmx1.06mm) package . Acceleration and temperature data can be stored in a 512-sample multimode FIFO buffer, allowing data to be saved for up to 13 seconds.
ADI has developed a demonstration-only VSM watch (shown in Figure 11) to demonstrate the potential of ultra-low-power devices such as the ADXL362 in battery-powered and space-constrained applications.
Figure 11. VSM watch (integrating multiple ADI devices, designed to highlight ultra-low power, small size and lightweight products)
The ADXL362 is used to track and record motion, helping to remove interference artifacts from other measurements.
Condition Monitoring (CBM)
Key metrics: low noise, wide bandwidth, signal processing, g-range and low power consumption.
CBM needs to monitor several parameters, such as machine vibration, to detect and indicate possible failures. CBM is an important part of preventive maintenance, and its technology is often used to drive machinery such as turbines, fans, pumps, motors, etc. The key specifications of CBM accelerometers are low noise and wide bandwidth. At the time of this writing, there are very few competing companies offering MEMS accelerometers with bandwidths above 3.3kHz, with some specialty manufacturers offering bandwidths up to 7kHz.
With the development of the Industrial Internet of Things, the industry is placing increasing emphasis on reducing wiring and utilizing wireless, ultra-low-power technologies. This puts MEMS accelerometers ahead of piezoelectric accelerometers in terms of size, weight, power consumption, etc., and has the potential to enable integrated smart features. The most commonly used sensors in CBM are piezoelectric accelerometers because of their good linearity, SNR, high temperature operation, and wide bandwidth (typically 3 Hz to 30 kHz, and in some cases up to hundreds of kHz). However, piezoelectric accelerometers do not perform well in the DC range (as shown in Figure 12), so there can be numerous failures in the lower frequency to DC range, especially in wind turbines and similar low RPM applications. The mechanical properties of piezoelectric sensors make them difficult to mass-produce like MEMS, and they are more expensive and less flexible in terms of interface and power supply.
MEMS capacitive accelerometers have higher integration and richer functions, support self-test, peak acceleration, spectrum alarm, FFT and data storage, shock resistance up to 10000g, DC response capability, and smaller size and weight lighter. The ADXL354/ADXL355 and ADXL356/ADXL357 have ultra-low noise and excellent temperature stability, making them ideal for condition monitoring applications, but their bandwidth limitations prevent deeper diagnostic analysis. However, even with a limited bandwidth, these accelerometers can provide important measurements; for example, in the condition monitoring of wind turbines where equipment rotates at extremely low speeds. In this case, a response down to DC is required.
Figure 12. Rotating equipment failure vibration artifacts.
The ADXL100x family of single-axis accelerometers is optimized for industrial condition monitoring applications, with a measurement bandwidth of up to 50kHz, a g range of up to ±100g, and ultra-low noise performance—making it comparable to piezoelectric accelerometers in performance.
The frequency response of the ADXL1001/ADXL1002 is shown in Figure 13. Major faults that occur in rotating machinery such as sleeve bearing damage, misalignment, imbalance, friction, looseness, transmission failure, bearing wear and cavitation are all within the measurement range of the ADXL100x Series condition monitoring accelerometers.
Figure 13. Frequency response, high frequency (>5kHz) vibration response of the ADXL1001/ADXL1002; the laser vibrometer controller is referenced to the ADXL1002 package for improved accuracy.
Piezoelectric accelerometers typically do not integrate intelligence, while MEMS capacitive accelerometers (such as the ADXL100x series) integrate an overrange detection circuit that provides an alarm when a severe overrange event occurs that exceeds approximately 2 times the specified g range. . This feature is critical in the development of intelligent measurement and monitoring systems. The ADXL100x employs some kind of internal clock smart disable mechanism to protect the sensor element in the event of persistent overrange events, such as can occur when a motor fails. This approach can offload the host processor and increase the intelligence of a sensor node—both key metrics for condition monitoring and IIoT solutions.
MEMS capacitive accelerometers have made huge leaps in performance, and as a result, the new ADXL100x family has begun to compete strongly and capture the ground previously dominated by piezoelectric sensors. The ADXL35x family has the industry’s best ultra-low noise performance and can also replace sensors in CBM applications. New CBM solutions and models have begun to merge with IoT architectures to form better detection, connectivity, and storage and analysis systems. ADI’s newest accelerometers will bring smarter monitoring to edge nodes, enabling factory managers to implement fully integrated vibration monitoring and analysis systems.
Complementing these MEMS accelerometers are the first-generation CBM subsystems, the ADIS16227 and ADIS16228 semi-autonomous fully integrated wide-bandwidth vibration analysis systems (shown in Figure 14); Band programmable alarms, 2-level alarm and fault definition settings, adjustable response delay to reduce false alarms, internal self-test with status flags, and more. Frequency domain processing includes 512-point, real-valued FFT for each axis, and FFT averaging, the latter of which reduces noise floor variation for improved resolution. The ADIS16227 and ADIS16228 fully integrated vibration analysis systems reduce design time, cost, processor requirements, and space constraints, making them ideal for CBM applications.
Figure 14. Digital triaxial vibration sensor with integrated FFT analysis and storage system.
IoT/Wireless Sensor Networks
Key metrics: power consumption, integrated features for smart power saving and measurement, small size, deep FIFO, and suitable bandwidth.
The entire industry is well aware of the promise of IoT. To realize this prospect, millions of sensors will need to be deployed in the coming years. The vast majority of these sensors will be installed in inconvenient or space-constrained locations (such as rooftops, streetlight tops, tower masts, bridges, inside heavy machinery, etc.) to realize concepts such as smart cities, smart agriculture, and smart buildings. Due to constraints such as these, it is likely that a large proportion of these sensors will require wireless communication, battery power, and possibly some form of energy harvesting.
The trend in IoT applications is to minimize the amount of data that is transmitted wirelessly to the cloud or on-premises servers for storage and analysis, as existing methods require high bandwidth and are expensive. Through intelligent processing at sensor nodes, useless data can be distinguished from useful data, reducing the need to transmit large amounts of data, thereby reducing bandwidth and cost requirements. This requires sensors to be smart while maintaining ultra-low power consumption levels. A standard IoT signal chain is shown in Figure 15. Beyond the gateway, ADI provides solutions for individual modules. Note that not all solutions require wireless connectivity, for many applications a wired solution is still necessary, be it RS-485 interface, 4mA to 20mA, Industrial Ethernet, etc.
Figure 15. ADI’s edge sensor node solution.
Once the nodes have some intelligence, only the useful data can be transmitted through the signal chain – saving power and bandwidth. In CBM, the amount of processing done locally at the sensor nodes depends on several factors, such as the cost and complexity of the machine and the cost of the condition monitoring system. The transmitted data varies from simple out-of-range alarms to data streams. Standards such as ISO10816 stipulate corresponding alarm conditions. When a machine of a given size runs at a specific RPM speed, if the vibration speed exceeds a preset threshold, the machine will output an alarm signal. The purpose of ISO10816 is to optimize the useful life of the system under test and its rolling bearings, thus reducing the amount of data transmitted to provide better support for deployment in a WSN architecture.
Accelerometers used in ISO10816 applications require a g range of 50g or less and low noise at low frequencies as the system periodically integrates the acceleration data to form a single velocity in mm/secrms point. When integrating accelerometer data that contains low frequency noise, the error in the velocity output may increase linearly. The ISO standard specifies a measurement range of 1Hz to 1kHz, but users want to integrate data down to 0.1Hz. Traditionally, in charge-coupled piezoelectric accelerometers, this has been limited by high noise levels at low frequencies, but ADI’s next-generation accelerometers keep the noise floor as low as dc, limited only by the 1/f noise of the signal conditioning electronics The limit of the corner frequency can be reduced to 0.01Hz through careful design. MEMS accelerometers can be used in economical CBM applications for low-cost devices or integrated into embedded solutions due to their smaller size and lower cost compared to piezoelectric sensors.
ADI’s broad range of accelerometer products is ideal for smart sensor nodes requiring ultra-low power consumption, incorporating features that help extend battery life, reduce bandwidth usage, and therefore lower costs. Some of the key metrics for IoT sensor nodes are low power consumption (ADXL362, ADXL363) and a rich feature set to enable energy management and specific data detection such as threshold crossing activity, spectral line profile alerts, peak acceleration values and very long activity or Inactive (ADXL372, ADXL375).
All of these accelerometers can keep the entire system in a shutdown state while storing the acceleration data in the FIFO and checking for activity events. When a shock event occurs, the data collected before the event is frozen in the FIFO. Without a FIFO, capturing samples before an event would require the processor to continuously sample and process the accelerated signal, resulting in a significant reduction in battery life. The ADXL362 and ADXL363 FIFOs can store more than 13 seconds of data, thus providing a clear representation of what happened before an activity was triggered. Instead of using a power duty cycle, the full bandwidth architecture at all data rates prevents input signal aliasing and maintains ultra-low power consumption.
Asset Condition Monitoring
Key metrics: power consumption, integrated features for smart power saving and measurement, small size, deep FIFO, and suitable bandwidth.
Asset Condition Monitoring (AHM) generally refers to the monitoring of high-value assets over a period of time, whether at rest or in transit. These assets may be cargo in shipping containers, long-range pipelines, civilians, soldiers, high-density batteries, etc., which are vulnerable to impact or impact events. IoT provides an ideal reporting infrastructure for such events that could impact asset functionality or safety. For the sensors used in AHMs, the ability to measure asset-related high-g shocks and shock events while maintaining ultra-low power consumption is key. Other key sensor metrics to consider when embedding these types of sensors in battery-operated or portable applications include size, oversampling, and anti-aliasing features designed to accurately handle high-frequency components, as well as various smart features to increase Host processor sleep time and allow interrupt driven algorithms to detect and capture shock characteristics extending battery life.
The ADXL372 micropower ±200g MEMS accelerometer addresses the emerging asset condition monitoring market’s need for smart IoT edge nodes. The device contains several unique features developed specifically for the asset condition monitoring market to simplify system design and achieve energy savings at the system level. High-g events, such as shocks or impacts, are often strongly associated with acceleration components at wider frequencies. Accurate capture of these events requires a wide bandwidth, as making measurements under bandwidth-hungry conditions can significantly reduce the magnitude of the recorded events, leading to errors. This is a key parameter to pay special attention to in the data sheet. Some devices do not meet the Nyquist sampling rate standard. The ADXL375 and ADXL372 offer the option to capture the entire shock characterization, which can be used for further analysis without host processor intervention. This functionality is achieved using the shock interrupt register in combination with the accelerometer’s internal FIFO. As shown in Figure 16, it is important to have sufficient FIFO in order to characterize the shock prior to a trigger event. If the FIFO is insufficient, shock events cannot be recorded and maintained for further analysis.
Figure 16. Accurate capture of shock characteristics.
The ADXL372 operates with a bandwidth of 3200Hz at ultra-low power levels. The steep filter roll-off also facilitates effective rejection of out-of-band components, for which the ADXL372 integrates a four-pole low-pass antialiasing filter. Without antialiasing filtering, any input signal whose frequency exceeds half the output data rate will alias into the target measurement bandwidth, resulting in measurement errors. The four-pole low-pass filter provides user-selectable filter bandwidth, which provides great flexibility for user applications.
With the instant-on shock detection feature, the user can configure the ADXL372 to capture shock events above a certain threshold in an ultra-low power mode. As shown in Figure 17, after a shock event, the accelerometer enters full measurement mode in order to accurately capture shock characteristics.
Figure 17. Instant-on mode at default thresholds.
Some applications require that only peak acceleration samples from shock events be recorded, as such samples alone provide sufficient information. The ADXL372FIFO can store peak acceleration samples for each axis. The maximum duration that can be stored in the FIFO is 1.28 seconds (512 single-axis samples at 400Hz ODR). 170 3-axis samples at 3200Hz ODR correspond to a 50ms time window, which is sufficient to capture typical shock waveforms. For applications that do not require full event characteristics, further power savings can be achieved by storing only peak acceleration information, which can significantly increase the time between FIFO read operations. The 512 FIFO samples can be allocated in a number of ways, including the following:
• 170 sample sets of parallel 3-axis data
• 256 sample sets for parallel 2-axis data (user selectable)
• 512 sample sets for single axis data
• 170 shock event peak sets (x,y,z)
Proper use of FIFOs can reduce system-level power consumption by allowing the host processor to sleep for extended periods of time while the accelerometer collects data autonomously. Alternatively, using a FIFO to collect data can offload the host processor, allowing it to handle other tasks.
There are several other accelerometers on the market with similar high-g performance, but they are not suitable for AHM/SHM IoT edge node applications due to their narrower bandwidth and higher power consumption. When low power modes are available, it is generally low bandwidth that cannot be measured accurately. The ADXL372 truly enables a use-and-forget AHM/SHM implementation model, prompting end customers to reconsider potential asset classes where feasible.
ADI offers a wide range of accelerometer products for a variety of applications, some of which are not discussed in this article, such as dead reckoning, AHRS, inertial measurement, automotive stability and safety, medical alignment, and more. Our new generation of MEMS capacitive accelerometers are ideal for applications requiring low noise, low power, high stability, and temperature stability; featuring low compensation and integrating numerous smart features to improve overall system performance and reduce design complexity Spend. ADI provides all relevant data sheet information designed to help you choose the most suitable device for your application. All devices listed above and others are available for evaluation and prototyping.
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