Accurately estimating the remaining useful life (RUL) of industrial machinery is beneficial in many real-world applications. Estimation techniques have mainly utilized linear models or neural network based approaches with a focus on short term time dependencies. This paper introduces a system model that incorporates temporal convolutions with both long term and short term time dependencies. The proposed network learns salient features and complex temporal variations in sensor values, and predicts the RUL. A data augmentation method is used for increased accuracy. The proposed method is compared with several state-of-the-art algorithms on publicly available datasets. It demonstrates promising results, with superior results for datasets obtained from complex environments.

One thing about airplanes--especially ones that fly from aircraft carriers, where they're battered by saltwater and tough deck landings--is that they need lots of spare parts that are not always on hand. Instead of flying in new parts, though, future Navy ships may be able to make new ones to order. Picutre an intelligent, laser-wielding robot that can analyze the damage and 3D-print the needed titanium alloy parts from an onboard supply of metallic dust. This is one glimpse of the future proposed by the Office of Naval Research (ONR), which today announced a two-year, $5.8 million contract to create a new generation of super-smart 3D printers. The printers would not only make parts on order wherever they are needed, but can "observe, learn and make decisions by themselves," according to Lockheed.

For years, Lawrence Livermore National Laboratory engineers and scientists have used an array of sensors and imaging techniques to analyze the physics and processes behind metal 3-D printing in an ongoing effort to build higher quality metal parts the first time, every time. Now, researchers are exploring machine learning to process the data obtained during 3-D builds in real time, detecting within milliseconds whether a build will be of satisfactory quality. In a paper published online Sept. 5 by Advanced Materials Technologies, a team of Lab researchers report developing convolutional neural networks (CNNs), a popular type of algorithm primarily used to process images and videos, to predict whether a part will be good by looking at as little as 10 milliseconds of video. "This is a revolutionary way to look at the data that you can label video by video, or better yet, frame by frame," said principal investigator and LLNL researcher Brian Giera. "The advantage is that you can collect video while you're printing something and ultimately make conclusions as you're printing it. A lot of people can collect this data, but they don't know what to do with it on the fly, and this work is a step in that direction."

What's possible with 3D printing is largely driven by two things: How patient you are, since printing large or complex structures can take a while, and what kind of build volume you have to work with. Most 3D printers are boxes, and inside those boxes are smaller boxes, and inside those boxes are the area in which a thing can be printed. If your thing is larger than that box, you've either got to print it in pieces that can be assembled later, buy yourself a new printer, or give up entirely. You can certainly 3D print very large things, but there are still usually build volume constraints. We've seen examples of robot arms that can print anywhere they can reach, as well as gantry systems that can print structures like houses, as long as the structures are slightly smaller than they are.

There are a lot of changes that occur in companies under the influence of the information technology innovations. Those changes help significantly increase the quality of products and services, which increases the level of customer loyalty and satisfaction. Manufacturers also do not stand aside. New approaches and business models born in Industry 4.0 allow them increasing profit and investing more in the product enhancement. The term "industry 4.0" is now used as a synonym for the fourth industrial revolution.

Today, you will find 3D printers in the most surprising places--and all over the world. Not only that, but they are often busy doing the most surprising things for the human race. If you have been following 3D printing for even the shortest amount of time, then you may have learned to continually expect the unexpected. Machine learning and data calculations are perfect examples of this as they are now being applied in 3D via a new artificial intelligence system that performs its work through bending light. AI is built on looping calculations of numbers and data that ultimately result in recognition.

An AI system can enable such services as predictive maintenance, which multiplies the value of the Internet of Things (IoT). "With AI, users can learn patterns that lead to failures and make predictions such as construction equipment failing if it is not serviced after a certain amount of time," Maciej Kranz, of Cisco, explains. "The AI system might also recommend how to operate the equipment to maximize its useful life, offering trade-offs between performance and longevity." Machine learning makes the analytics systems "smarter" as time goes on and more data sets and patterns are available. Kranz makes the analogy that AI is the brain and IoT is the body, with IoT providing both input (data) and output (action) for the smart computing and analytics function of a centralized AI system.

Among emerging technologies, IT professionals expect Internet of Things (IoT) devices and artificial intelligence (AI) technology to have the biggest impact in the workplace, according to a new report from online IT professionals community Spiceworks. The study, "Future of IT: Hype vs. Reality," examines organizations' adoption plans for technologies such as virtual reality (VR), 3D printing, IoT and AI. While survey respondents don't expect mass adoption to take off for VR and 3D printers, some industries have significantly higher adoption rates than the industry average, the report notes. Of the 566 IT professionals surveyed worldwide in October 2016, 80 percent said IoT devices will be useful to their business practices in three to five years, and nearly 60 percent said the same for AI. Over the next five years 60 percent of companies plan to adopt machine learning and 72 percent plan to deploy business analytics with AI.

Digital manufacturing is rapidly changing the fundamentals of how products are developed, scaled and manufactured. By digitizing traditional manufacturing methods, including injection molding and CNC machining, and leveraging newer technologies, like 3D printing, the industrial internet of things (IIoT) and artificial intelligence (AI), companies are optimizing their supply chains, reducing development cycles, increasing efficiencies, and driving down costs. The Fourth Industrial Revolution continues to gain traction and is completely changing the economics of manufacturing, for those willing to embrace the change that is. Digital manufacturing breaks down barriers from traditional methods and reduces risk in product development, enabling manufacturers to bring products to market faster, and more effectively than ever before.Protolabs Empowered by connectivity and social networks, consumers are demanding more personalization than ever before. A Deloitte report noted half of consumers are interested in customized products and would be willing to pay more and wait longer if they could have an active role in design. The move from mass production to mass customization has historically had high cost implications, but the balance is beginning to tilt.

Driverless AI, the automated machine learning platform from H2O.ai, is now available for ordering through IBM . For a more comprehensive description of Driverless AI, see the H2O.ai website. IBM and H2O.ai collaborate to enable you to order Driverless AI directly from IBM. H2O Driverless AI is a high-performance, GPU-enabled software application for the rapid development and deployment of advanced predictive analytics models. It lowers the barrier to entry for machine learning by automating a large portion of the process of algorithm selection and model building and tuning. Driverless AI uses machine learning interpretability to create easy-to-follow visualization and explanations of models, which are especially useful in regulated industries.