Developing Wireless Sensor System for Continuous Monitoring of Bridge Deformation

 Towards infrastructure sensing, monitoring and maintenance.

Researchers develop wireless sensor system for continuous monitoring of bridge deformation   by Drexel University

Researchers in Drexel University's College of Engineering have developed a solar-powered, wireless sensor system that can continually monitor bridge deformation and could be used to alert authorities when the bridge performance deteriorates significantly. With more than 46,000 bridges across the country considered to be in poor condition, according to the American Society of Civil Engineers, a system like this could be both an important safety measure, as well as helping to triage repair and maintenance efforts.

The system, which measures bridge deformation and runs continuously on photovoltaic power, was unveiled in a recent edition of the IEEE Journal of Emerging and Selected Topics in Industrial Electronics in a paper authored by Drexel College of Engineering researchers, Ivan Bartoli, Ph.D., Mustafa Furkan, Ph.D., Fei Lu, Ph.D., and Yao Wang, a doctoral student in the College.

"With as much aging infrastructure as there is in the U.S. we need a way to keep a close eye on these critical assets 24/7," said Bartoli, who leads the Intelligent Infrastructure Alliance in the College of Engineering. "This is an urgent need, not just to prevent calamitous and often tragic failures, but to understand which bridges should take priority for maintenance and replacement, so that we can efficiently and sustainably approach the preservation and improvement of our infrastructure."

More than 40% of America's 617,000 bridges are more than 50 years old. While they are built to last, they must also be inspected regularly—every two years, according to Bartoli, who is a professor in the College.

Software Maintenance Mistake Center of Major FAA Computer Meltdown

Good to examine past issues, in particular maintenance that often is not done considering initial design.

Software Maintenance Mistake Center of Major FAA Computer Meltdown    ABC News,   Sam Sweeney; Jon Haworth; Kevin Shalvey, January 11, 2023; et al.

A senior official at the U.S. Federal Aviation Administration (FAA) blamed a software maintenance error for a computer breakdown this past week that grounded domestic flights. FAA officials said earlier the affected Notice To all Air Missions (NOTAM) system sends notifications to pilots of flight hazards and real-time restrictions. The senior official said the breakdown occurred when an engineer unwittingly "replaced one file with another," triggering a series of cascading information technology failures because an updated NOTAM system that lacked redundancies was not in place. The FAA emphasized that a cyberattack was not indicated, while Transportation Secretary Pete Buttigieg said a full investigation is required to prevent future mistakes.  ... '

Troubleshoot Your Slow PC

Happened on this, seems useful.  Sometimes obvious,   Passed it on. 

How to Troubleshoot Your Slow PC  in ExtremeTech

By Joel Hruska on September 27, 2021 at 12:20 pm

Speeding up a slow PC can be a challenge, particularly when dealing with older hardware that may be on the cusp of needing an upgrade or replacement anyway. Sometimes, a system simply needs a fresh OS install or driver update to perform significantly better. In other cases, upgrade or wholesale replacement are necessary.  ... '  

Maintaining Neural Networks

 Models will degrade, how do you maintain them?  

Researchers Discover How to Predict Degradation of Neural Network

DiariDigital URV Activ@, February 16, 2022

Researchers at Spain's Universitat Rovira i Virgili (URV) have identified the theoretical underpinnings for predicting how neural networks will function and degrade over time. These findings indicate how much damage a system can endure before it will completely degrade and lose its functionality, known as the phase transition of percolation degradation. URV's Alex Arenas said, "We have been able to find this transition and we have also been able to calculate the homeostatic response [i.e., the ability to find alternatives and continue functioning] of the network." He added that a set of mathematical tools "that can be very useful not only in neuroscience but in any type of network" is now available to the scientific community.  ... ' 

AI Needs to Selectively Forget

We discovered his early on as we had to effectively maintain models.

Can AI Learn to Forget?     By Samuel Greengard  in the CACM

Communications of the ACM, April 2022, Vol. 65 No. 4, Pages 9-11   10.1145/3516514

Machine learning has emerged as a valuable tool for spotting patterns and trends that might otherwise escape humans. The technology, which can build elaborate models based on everything from personal preferences to facial recognition, is used widely to understand behavior, spot patterns and trends, and make informed predictions.

Yet for all the gains, there is also plenty of pain. A major problem associated with machine learning is that once an algorithm or model exists, expunging individual records or chunks of data is extraordinarily difficult. In most cases, it is necessary to retrain the entire model—sometimes with no assurance that that model will not continue to incorporate the suspect data in some way, says Gautam Kamath, an assistant professor in the David R. Cheriton School of Computer Science at the University of Waterloo in Canada.

The data in question may originate from system logs, images, health records, social media sites, customer relationship management (CRM) systems, legacy databases, and myriad other places. As right to be forgotten mandates appear, fueled by the European Union's General Data Privacy Regulation (GDPR) and the California Consumer Privacy Act (CCPA), organizations find themselves coping with potential minefields, including significant compliance penalties.

Not surprisingly, completely retraining models is an expensive and time-consuming process, one that may or may not address the underlying problem of making sensitive data disappear or become completely untraceable. What's more, there frequently is no way to demonstrate the retrained model has been fully corrected, and that it is entirely accurate and valid.

Enter machine unlearning. Using specialized techniques—including slicing databases into smaller chunks and adapting algorithms—it may be possible to induce selective 'amnesia' in machine learning models. The field is only beginning to take shape. "The goal is to find a way to rebuild models on the fly, rather than having to build an entirely new model every time the data changes," says Aaron Roth, a professor of computer and information science at the University of Pennsylvania.  ... ' 

Training as Maintenance

Much of my early work  in the enterprise dealt with predicting maintenance in plant systems.  So this article is of interest.  Mckinsey's take, reasonable.   Can in particular agree it makes sense to capture lots of time based data and to continually upgrade the way this data is mined.  We even tested some of the methods with human data ... can training be considered a maintenance task?  

 Prediction at scale: How industry can get more value out of maintenance  from McKinsey

Machines can now tell you when they aren’t feeling well. The challenge for today’s industrial players lies in applying advanced predictive-maintenance technologies across the full scope of their operations.  ... ' 

Hubble Fixed!

Consider the amount of knowledge and data involved!    Great if this comes through.   Also to the fact that such hardware and software can be remotely changed.  Also a hint to how remote maintenance can be planned for and done. 

Hubble Is Back!'   By Science  July 19, 2021

Following a switch from the operating payload control computer to a backup device, Hubbles operators re-established communications with all the telescopes instruments.

Credit: U.S. National Aeronautics and Space Administration

The iconic but elderly Hubble Space Telescope appears to have been resurrected again after a shutdown of more than a month following a computer glitch. Science has learned that following a switch from the operating payload control computer to a backup device over the past 24 hours, Hubble's operators have re-established communications with all the telescope's instruments and plan to return them to normal operations today.

"Hubble is back!" Tom Brown, head of the Hubble mission office, emailed to staff at the Space Telescope Science Institute at 5:56 a.m. "I am excited to watch Hubble get back to exploring the universe."

From Science     Full Article

Turn a Single Image into a Looping Video: Uses?

 Experimented with something like this.  Took a single image of part of a machine or process, then applying some images of specific known maintenance issues,  and apply likelihood under specific contexts. Then show to a human expert for analysis.  NOT the same thing, we integrated much more information.  But I can see this method integrated for broader use ... say deriving a short video of the maintenance issue.  Thinking other uses of such constructed animation in further 'derived' animation. 'Image learning"?    What else can help derive a fuller image?   ...

UW Researchers Can Turn a Single Photo into a Video,  By University of Washington News

A new deep learning method can convert a single photo of any flowing material into an animated video running in a seamless loop.   University of Washington (UW) researchers invented the technique, which UW's Aleksander Holynski said requires neither user input nor additional data.

The system predicts the motion that was occurring when a photo was captured, and generates the animation from that information. The researchers used thousands of videos of fluidly moving material to train a neural network, which eventually was able to spot clues to predict what happened next, enabling the system to ascertain if and in what manner each pixel should move.

The team's “systemic splatting” method forecasts both the future and the past for an image, then blends them into one animation.

Full article 

From University of Washington News   ... 

Adjusting Fare Algorithms

 This came up in discussion this week .. the algorithms are classic approaches.  But the algorithms are adaptive, so I would expect them to be continually adjusted. And if they are maintaining them, as I always suggest .... 

COVID-19 Wrecked the Algorithms That Set Airfares, but They Won't Stay Dumb

The Wall Street Journal, Jon Sindreau, May 17, 2021

The COVID-19 pandemic crippled the reliability of algorithms used to set air fares based on historical data and has accelerated a hybrid model that combines historical and live data. Before the pandemic, airlines used the algorithms to predict how strong ticket demand would be on a particular day and time, or exactly when people will fly to visit relatives before a holiday. Corporate travel constitutes a large share of airline profits, with business fliers avoiding Tuesdays and Wednesdays, favoring short trips over week-long ones, and booking late. The pandemic undermined historical demand patterns while cancellations undercut live data, causing the algorithms to post absurd prices. Overall, the pandemic has stress-tested useful advancements to the algorithms, like assigning greater weight to recent booking numbers, and applying online searches to forecast when and where demand will manifest.  ... '

How and Why to Share Scientific Code

Rarely done this consistently, but it is a useful approach to follow.   Add it to a review and follow up of results.   Plans for maintaining models.  

How and why to share scientific code

A simple guide to reproducible research without becoming a software engineer

By Nathan C. Frey

When you do an experiment, whether that’s in a lab or on a computer, you generate data that needs to be analyzed. If your analysis involves new methods, algorithms, or simulations, you probably wrote some code along the way. Scientific code is designed to be quick to write, easy for the writer to use, and never looked at again after the project is complete (maybe designed is a strong word).

For many scientists, packaging their code involves a lot of work and no reward. I want to share a few obvious benefits and some that are hopefully non-obvious. After that, I’ll give some tips for how to share your code as painlessly as possible without detouring into becoming a software engineer. If you want a simple example of what the finished product will look like, check out my repos for Python Topological Materials or Positive and Unlabeled Materials Machine Learning.

The benefits of sharing scientific code

Encourage reproducibility. As soon as a method has more than one step (click the big red button) or a data analysis pipeline is more complex than “we divided all the numbers by this number,” it becomes unlikely that other scientists will be able to really explore what you did. If you developed a set of instructions to process or generate your data, you wrote a program, whether you wrote it down in code or not. It’s much more natural to share that program than to only write out what you did in your paper.  ... " 

Quanta Magazine: Brain Rotates Memories to Save Them

 Made me think of how this kind of structure could be used in machine learning to perform a kind of  maintenance and change managemebt as new data is acquired.  Even lead to creativity based on current knowledge. 

The Brain ‘Rotates’ Memories to Save Them From New Sensations

Jordana Cepelewicz   Quanta Mag Staff Writer

Some populations of neurons simultaneously process sensations and memories. New work shows how the brain rotates those representations to prevent interference. 

During every waking moment, we humans and other animals have to balance on the edge of our awareness of past and present. We must absorb new sensory information about the world around us while holding on to short-term memories of earlier observations or events. Our ability to make sense of our surroundings, to learn, to act and to think all depend on constant, nimble interactions between perception and memory.

But to accomplish this, the brain has to keep the two distinct; otherwise, incoming data streams could interfere with representations of previous stimuli and cause us to overwrite or misinterpret important contextual information. Compounding that challenge, a body of research hints that the brain does not neatly partition short-term memory function exclusively into higher cognitive areas like the prefrontal cortex. Instead, the sensory regions and other lower cortical centers that detect and represent experiences may also encode and store memories of them. And yet those memories can’t be allowed to intrude on our perception of the present, or to be randomly rewritten by new experiences.

A paper published recently in Nature Neuroscience may finally explain how the brain’s protective buffer works.  https://www.nature.com/articles/s41593-021-00821-9   .... ' 

Automatically Updating Facts

 When we sought to construct a company Wiki, we found one of the most important issues, after validating it, was updating knowledge.  Here MIT CSAIL is looking at that problem.

Auto-Updating Websites When Facts Change

MIT Computer Science and Artificial Intelligence Laboratory

March 29, 2021

Massachusetts Institute of Technology (MIT) researchers have developed models to reduce the amount of incorrect or outdated information online and dynamically adjust to recent changes. The researchers used deep learning models to rank an initial set of about 200 million revisions to popular English-language Wikipedia pages. Annotators found about a third of the top 300,000 revisions included a factual difference. The researchers created a model to mimic the filtering performed by human annotators, which can detect nearly 85% of revisions that include a factual change. They also created a model to automatically revise texts and suggest edits to other articles, as well as a robust fact verification model. MIT's Tal Schuster said, "Instead of teaching the model that the population of a certain city is this and this, we teach it to read the current sentence from Wikipedia and find the answer that it needs." ... '

Retrain Your Model, Update the Data

Such an obvious thing, but often forgotten. I make a point to include such updating instructions in everything I delivered.   No data, except from physics, is static.  Estimate, at least, the risk involved in not updating a model consistently.    It also applies to all analytical models, beyond AI/ML models.  We did it in common optimization models in the enterprise.  

Why is re-training ML Models Important?

A Product Manager’s Perspective  in Towards Data Science

By Humberto Corona (totó pampín)

As a product manager, you are responsible for measuring the continuous success of your product. That might include validation before launching, measuring uplifts in an A/B test while launching, and keeping track of core KPIs. If you are managing a Machine Learning product, the long-term success of your product will depend on keeping your models up-to-date. In this post I explain why this is important problem and how can you ensure that continuous success through model re-training.  ... "

Bringing Power Tool From Math Into Quantum Computing

The implication that this idea can be used for problems already well solved by FFT methods, is considerable.  These kinds of pattern recognition techniques are already well known in engineering applications.    Would like to try this against some problems like machine maintenance.

Bringing Power Tool From Math Into Quantum Computing  Tokyo University of Science (Japan),   October 14, 2020

Scientists at Japan's Tokyo University of Science (TUS) have designed a novel quantum circuit that calculates the fast Fourier transform (FFT) in a faster, versatile, and more efficient manner than previously possible. The quantum fast Fourier transform (QFFT) circuit does not waste any quantum bits, and it exploits the superposition of states to boost computational speed by processing a large volume of information at the same time. Its versatility is another benefit. TUS' Ryoko Yahagi said, "One of the main advantages of the QFFT is that it is applicable to any problem that can be solved by the conventional FFT, such as the filtering of digital images in the medical field or analyzing sounds for engineering applications."

WalMart Competing with Geek Squad

Done well and priced well could attract and engage many consumers.   

Has Walmart come up with an answer to Best Buy’s Geek Squad?  In Retailwire by George Anderson

Walmart is taking part in a pilot program to test the viability of offering consumer electronics and technology services to its customers at a fraction of what similar services such as Best Buy’s Geek Squad cost.

The retailer is setting up kiosks — four Dallas-area locations and another in Springdale, AR — that will enable customers to sign up for in-home installation of computing devices, smart home products, televisions and WiFi. It will also offer repair services for damaged smartphones and other electronic devices.

Walmart is looking to roll out the service in 50 locations by the middle of next year, and shoppers in areas with the service will be able to access it online. The kiosks will be staffed by True Network Solutions, which is partnering with the retailer to provide the service.  ... "

Maintaining the Measures

Like the general thought, measures are important, but what is driving and changing the measures?  Another example of maintaining the model in use.  Changes will happen.

Modern IT KPIs emphasize cloud, DevOps and user experience

When it comes to KPIs, IT ops teams have typically prioritized process-centric metrics, but recent technical and cultural shifts have started to change that.

By Alyssa Fallon, Assistant Site Editor

Key performance indicators drive IT operations teams to work more efficiently -- but what drives KPIs?

While specific metrics will always vary between organizations, the IT KPIs that enterprises track are evolving as a whole. Technical initiatives, such as cloud and DevOps adoption, as well as organizational changes that emphasize IT-business alignment, have IT shops eyeing a new, or at least more expansive, set of key performance indicators.

Technical drivers
The rise of cloud and DevOps has transformed IT in many ways -- including KPIs.

"DevOps has a unique set of KPIs in terms of how applications are developed, how they are provisioned, how they are maintained, how they operate, how frequently they may fail or need to change, and how quickly they can be fixed," said Carl Lehmann, principal analyst at 451 Research. These unique DevOps KPIs include metrics such as time to provision, time to upgrade and time to value.... " 

Predictive Maintenance Driving 3D Printing

A long term interest and application area.  You could do a good job of prediction, but had to have a complex array of replacement parts in inventory.   Here, for the right kind of of application, the inventory could be minimized.  Could the parts even be produced to address certain kinds of predictive degradation by altering manufacturing design? 

Army 3D Printing Study Shows Promise for Predictive Maintenance
U.S. Army Research Laboratory
May 19, 2020

A study by researchers at the U.S. Army's Combat Capabilities Development Command (CCDC) Army Research Laboratory (ARL), the National Institute of Standards and Technology, CCDC Aviation and Missile Center, and Johns Hopkins University detailed a method for monitoring the performance of three-dimensionally (3D)-printed parts. The technique uses sensors to detect and track the wear and tear of 3D-printed maraging steel (known to possess superior strength and toughness without losing ductility), to help forecast degradation or malfunctions that warrant replacement. ARL's Todd C. Henry said the study was as much about understanding the specific performance of a 3D-printed material as it was about understanding the ability to monitor and detect the performance and degradation of 3D-printed materials.

Maintaining Energy Equipment

Very key thing in an industrial world.  And a good analytical view of the risk involved.

Maintaining the equipment that powers our world
By organizing performance data and predicting problems, Tagup helps energy companies keep their equipment running.

Zach Winn | MIT News Office

Most people only think about the systems that power their cities when something goes wrong. Unfortunately, many people in the San Francisco Bay Area had a lot to think about recently when their utility company began scheduled power outages in an attempt to prevent wildfires. The decision came after devastating fires last year were found to be the result of faulty equipment, including transformers.

Transformers are the links between power plants, power transmission lines, and distribution networks. If something goes wrong with a transformer, entire power plants can go dark. To fix the problem, operators work around the clock to assess various components of the plant, consider disparate data sources, and decide what needs to be repaired or replaced.

Power equipment maintenance and failure is such a far-reaching problem it’s difficult to attach a dollar sign to. Beyond the lost revenue of the plant, there are businesses that can’t operate, people stuck in elevators and subways, and schools that can’t open.

Now the startup Tagup is working to modernize the maintenance of transformers and other industrial equipment. The company’s platform lets operators view all of their data streams in one place and use machine learning to estimate if and when components will fail.  ... " 

Multi-Agent VR for Task Application?

Might this be useful for simulating multi-agent complex, interactive tasks in VR?    We encountered such problems when assigning multiple people to do a job and they needed to be informed of the location, status, actions ...   of others in the team.  This inhibited the use of VR solutions.   Like too that this could be done with simple devices.

Novel System Allows Untethered Multi-Player VR
Purdue University News  By Chris Adam

Purdue University researchers have created a virtual reality (VR) system that allows untethered multi-player gameplay on smartphones. The Coterie system manages the rendering of high-resolution virtual scenes to fulfill the quality of experience of VR, facilitating 4K resolutions on commodity smartphones and accommodating up to 10 players to engage in the same VR application at once. Purdue's Y. Charlie Hu said Coterie "opens the door for enterprise applications such as employee training, collaboration and operations, healthcare applications such as surgical training, as well as education and military applications."

AI and MRO Inventory Data

Another problem we often tangled with in the enterprise:


Maintenance Repair Operations

Artificial Intelligence wades through murky MRO Inventory Data to drive down costs with better business decisions

Webinar from Verusen and Accenture Details How AI and Predictive Analytics Clarify Data Across Multiple Systems to Right-Size Plant Inventories

ATLANTA, Jan. 30, 2020 /PRNewswire/ -- The ability of artificial intelligence (AI) to improve visibility of ERP inventory data, as well as the implementation timeline for using AI on a daily basis, were among the top interests of participants during a recent webinar, "Taking Stock of AI Technology for Inventory Management."

The webinar featured expert advice from CEO Paul Noble of Verusen, an innovator in materials inventory and data management technology, and Erik Green, practice lead, Materials & Equipment, at Accenture, a leading global professional services company.

"Reducing MRO inventory is one way for asset-intensive manufacturing industries to quickly drive value, but siloed data in ERP and other systems result in redundant parts—and understocking—that infrequent manual cleansing can't address to support dynamic business decisions," said Noble. "It's important for organizations to know that AI is a very real option today that can wade through data from multiple systems to present real-time suggestions for in-house teams—and then learn from those decisions."

The webinar demonstrated how AI replaces disconnected data silos with a digitized network footprint of all goods, services and logistics throughout the supply chain so inventory decisions are visible to teams across an organization. It covered how AI's machine-learning capability continually updates and interprets market trends to drive accurate predictive inventory analyses for indirect MRO, direct goods and even finished products, instead of relying on subjective decisions.  ... "