industrial equipment

PNI Sensor, a member of the SEMI-MSIG Positioning, Navigation and Timing (PNT) Technical Advisory Council, is developing advanced tracking systems that promise to increase industrial worker safety.The availability of low-cost GPS jamming and spoofing technologies renders GPS-only solutions for location and navigation an increasingly dangerous and ineffective choice for the dismounted soldier in a battlefield environment. This threat to armed forces has spurred development of new self-contained location and navigation technologies for defense applications — an innovation that offers significant advantages for commercial applications.Though not as complex and mission-critical as in defense, self-contained location technology is also essential in commercially available industrial applications. That’s particularly true for workers in industrial sectors such as utilities, mining, and construction, and in environments with lone or remote workers, such as first responders. While jamming and spoofing are not a threat in the industrial sector, determining the precise location of workers in GPS-denied environments is fundamental to ensuring their safety. This makes it a priority to adapt any self-contained, non-infrastructure-based location technology — which was first developed for the modern dismounted soldier — to industrial applications.Bodies in MotionInertial solutions are very difficult to implement properly, even without the challenges uniquely created by human motion dynamics. On a construction site, for example, workers tend to cover a wide range of disciplines: supervisors, electricians, iron workers and equipment operators, among others. While performing their jobs, construction workers change locations, both indoors and outdoors, and perform dynamic motion such as crawling, ducking and climbing. These are all motions that are very difficult to model using traditional adaptive filtering techniques, which are typically applied in vehicular inertial navigation platforms, such as aircraft, ships and tanks. Even if existing inertial navigation systems could be made size, weight, power and cost (SWaP-C)-compatible to be body-worn, their performance accuracy would still need to satisfy the application’s requirements. To properly determine a worker’s precise location to ensure safety on job sites and in remote locations, we must tackle the combined challenges of SWaP-c and human dynamic motion. That’s the most effective approach for creating a complementary positioning technology that augments GPS or other infrastructure-based location systems.To address these challenges, we need to build a high-performance inertial measurement solution using commercially available MEMS inertial sensors. The issues of bias drift error and low sensitivity have traditionally made such sensors practically useless for any meaningful inertial tracking. Fortunately, this is no longer the case. We now have sensors that already conform to the necessary SWaP-C requirements for the application, and have the additional advantage of high dynamic range of measurements without saturation errors, which helps to reduce high-force and rapid movement-induced errors, promoting greater accuracy.Thus, a path forward is emerging. The current generation of high-performance MEMS gyros can now inertially track workers’ locations to step-level resolution very well for up to 30 minutes — without significant location errors due to bias or scale errors. That’s an order of magnitude better than previous generations. With the new MEMS gyros, errors typically remain less than 2% of distance travelled over that time period. Strategically applying algorithm improvements with higher levels of magnetic corrections has the potential to bring that accuracy down even lower, to less than 0.5% of distance traveled for durations of one hour or more. What’s more, the improved gyro and accelerometer bias, gain, and signal-to-noise (SNR) performance allows for better magnetic anomaly rejection. This enables finer and more sustained gyro bias corrections in the fused solution, which creates a system greater than the sum of its parts. We believe that these newer systems will promote greater worker safety at a truly affordable price.PNI Sensor, a member of the SEMI-MSIG PNT Technical Advisory Council (TAC), is developing a tracking system that combines the best elements of the newest-generation MEMS devices with an electronic compass that uses advanced magnetic anomaly detection and rejection algorithms. Based on PNI’s latest attitude and heading reference system (AHRS), the novel PNT system employs a unique Kalman algorithm that intelligently fuses its reference magnetic sensors with gyros and accelerometers. In conjunction with this work, PNI Sensor has developed advanced pedometry functionality for use in its tracking system for very high dead-reckoning tracking performance used in defense industry applications. PNI is initially designing that system to track dismounted soldiers and special forces operating in GPS-denied or contested environments.For more information about PNI Sensor’s advanced location and navigation technology, please visit PNI Sensor. To learn more about the SEMI-MSIG PNT TAC, please contact Carmelo Sansone, director, MEMS Sensors Industry Group.George Hsu is a founder and CTO of PNI Sensor. He has focused his career on the sensor industry, having invented several magnetic sensor breakthroughs, including the magneto-inductive technology, the core of today’s electronic compassing in the automotive, consumer, scientific and military markets. Hsu is a graduate of Stanford University School of Engineering, holds several patents, and is a much-published author of technical articles on sensor theory, design and applications. He is an active member of the MEMS Sensors Industry Group PNT TAC.About the SEMI-MSIG Positioning, Navigation and Timing ProjectMEMS Sensors Industry Group (MSIG) created a member-based PNT TAC to identify and pursue PNT system innovations for GPS-denied environments. To that end, MSIG solicited proposals from its membership for the SEMI-MSIG PNT Project, a U.S. Army Research Laboratory-funded R D project. PNT committee members that have secured funding are pursuing R D platforms that improve accuracy and performance. Platforms may include software, hardware, and advanced packaging requirements of optical and MEMS-based positioning and timing systems.

Inertial sensors have continued to underpin the success of wearables in increasingly important ways. Propelled by evolutionary advancements in inertial sensors, wearables have strayed from their humble beginnings in simple activity and wellness, which defined the user experience over the past decade. What started with the simple act of telling people their daily step count has morphed to provide deeper insights into swim stroke and run cadence, all the way to mapping out a person’s off-piste ski route. Layered on top of this foundation of inertial sensors, we’ve fused optical, temperature and other sensor technology to provide clinical-grade healthcare snapshots available previously only by visiting the doctor’s office.Inertial sensors today are again leading the way in improving health and wellness. Instead of humans, however, this time the patients are machines. In fact, the health of critical assets – whether factory-based equipment, windmills, train bogies or aircraft – has been assessed through sophisticated analysis of their vibration signatures for many years. The sensors used for these applications have depended on piezoelectric technology because their vibration amplitude signals are very small and difficult to detect and because of the importance of understanding their spectral content over a wide bandwidth. When it comes to noise and bandwidth, bulk piezoceramics have had a major advantage over electrostatic MEMS technology – until recently.Using bulky expensive piezoelectric sensors for condition-based monitoring has been akin to going to the doctor’s office to have an MRI. The equipment required (sensors, receivers) is expensive and requires highly trained specialists to operate the machine and to interpret the information. For this reason, only mission-critical assets are instrumented. For nearly all other equipment, we tend to use inefficient schedule-based maintenance approaches to cover the gap of not having continuous data. Condition-based monitoring leverages real-time sensing of critical machine parameters to reduce system downtime and improve efficiency. Evolving machine healthMEMS started to democratize machine health several years ago, when suppliers began switching from piezoelectrics to capacitive MEMS. While the performance was still not on par with piezoelectric sensors, MEMS technology could already capture a wide array of faults. One example, the ADXL001, started making its way into Integrated Electronics Piezo-Electric (IEPE) and 4-20 mA sensors, which form the backbone of the vibration monitoring market. Although the bandwidth and noise of the sensor did not allow for very early detection and prescriptive monitoring, it did allow the tracking of faults as they progressed and became more imminent.Other digital accelerometers started finding their way into new wireless prototype systems with the goal to simplify and increase deployment to a greater population of assets. The thinking was that self-contained digital wireless sensor nodes could be deployed more economically and quickly, and that these digital sensors would bring the power of computing to the edge node.Unfortunately, even the lowest-noise MEMS products did not have the bandwidth needed to diagnose and predict faults early enough to influence how and when machines are maintained most economically. Instead, such devices were used to detect imminent failure to prevent irreparable harm. As we all know, however, the earlier the doctor spots a problem, the better the probable outcome. That’s because early detection increases the likelihood that the doctor will have access to the full spectrum of treatment options available to fix the problem.Inertial MEMS is blazing a new frontier with the introduction of next-generation capacitive MEMS such as the ADXL100x portfolio. Offering ultra-low noise density and high-frequency response, these newer capacitive MEMS devices fit the bill. With 3dB bandwidths up to 25 kHz and flat response curves within 0.4dB all the way to 10kHz, these accelerometers demonstrate compelling enabling characteristics such as better DC performance, improved robustness, lifetime stability, linearity, and of course, cost, making capacitive MEMS a better choice than piezoelectrics.With high-bandwidth capacitive MEMS much easier to use and deploy – as well as more affordable – the market is starting to respond. Condition monitoring equipment and instrumentation is becoming more accessible to a larger base of manufacturers. In turn, a wealth of data is being created and mined to develop better and timelier predictive and prescriptive maintenance approaches that rely heavily on machine learning and artificial intelligence (AI).It’s worth paying attention to the sizable condition-based monitoring market. Estimated at $3.5 billion and growing, condition-based monitoring reduces downtime and increases equipment utilization in quantifiable ways. And it’s not just manufacturers who stand to benefit. More sustainable and efficient industrial processes, safer trains that crisscross continents at ever increasing speeds, autonomous cars and trucks that know what’s happening under the hood as well as on the road, and modern infrastructure to support our evolving lives show us that condition-based monitoring has something for everyone.Learn more about Analog Devices’ condition-based monitoring signal-chain options that help customers on the journey from sensor to solution. View ADI’s whole portfolio of condition-based monitoring solutions online or download Next-Generation Condition-Based Monitoring brochure.Tzeno Galchev is product marketing manager in the Inertial Sensor Technology Group at Analog Devices Inc. He oversees the strategic marketing and product definition of the inertial sensor component portfolio. He received B.S. degrees in both Electrical and Computer Engineering in 2004, and M.S. and Ph.D. degrees in Electrical Engineering in 2006 and 2010 respectively from the University of Michigan, Ann Arbor. He has over 30 publications in the area of MEMS, holds multiple patents, and is a frequent lecturer and speaker on topics related to MEMS, energy harvesting and sensors.Analog Devices is a longtime member of MEMS Sensors Industry Group (MSIG), a SEMI technology community that enables the MEMS and sensor industry to address common challenges, innovate and accelerate business results.