Revolution On The Siemens Factory Floor


He is referring to the onset of the fourth industrial revolution, which the World Economic Forum predicted "will fundamentally alter the way we live, work and relate to one another. In its scale, scope and complexity, the transformation will be unlike anything humankind has experienced before." Fueled by advances in artificial intelligence, the Internet of Things and computing speed, businesses -- from auto to aerospace to retail -- are changing the fundamental building blocks of how they operate. By 2030, machine learning could contribute nearly $16 trillion to the global economy, research shows. For Mrosik and Siemens, the revolution is well underway.

Japanese companies hiking pay and holding classes in race to get tooled up on AI

The Japan Times

OSAKA – There's a sense of panic within Japan Inc. and the government -- the world's No. 3 economy, doesn't have enough experts in artificial intelligence, and it's time to do something about it. Prime Minister Shinzo Abe in June unveiled a plan to train 250,000 people in AI skills annually by 2025, albeit one criticized as unrealistic due to a shortage of teachers. Tech heavyweights like Sony Corp. are hiking pay for the right hires and boosting recruitment of foreign engineers. But Daikin Industries Ltd., the world's biggest maker of air conditioners with a market value of $37 billion, is taking a more unusual route to AI expertise. At a disadvantage to bigger tech firms in attracting top talent, it has created an in-house program that takes new graduates and current employees -- almost all with no AI background -- and trains them up.


USATODAY - Tech Top Stories

This reliable, feature-packed air conditioner from GE earned our top honors during testing. The GE Profile Series PHC08LY is a window-mounted air conditioner that blends top-notch cooling capabilities with a variety of unique features, with a bit of style and elegance. During testing, this 8,000 BTU (British Thermal Units) AC unit reduced our 340 square foot test area's temperature by 10 F in only 43 minutes and lowered the room's humidity by 14 percent in the same amount of time. On top of this, it ran (for an air conditioner) quietly. While using the GE Profile Series' Quiet Mode it only put out 49.3 dBA of sound -- that's less noise than an average household refrigerator makes.

Machines learn from machines Ingenuity Siemens


This is, what we aim to at our own factories. Read my introduction to a series of blogposts how we do this. Harnessing the power of artificial intelligence (AI), engineers at our manufacturing plant in Amberg can predict when a key component is likely to fail – up to 36 hours before the failure actually happens. This allows them to react in plenty of time to avoid a costly breakdown of the machine. In our electronics manufacturing facility in Amberg, we have several PCB cutting machines that are deployed for a number of our SIMATIC products – including the S7-300 and ET 200.

Air conditioner manufacturer taps AI to choose parts for repairs:The Asahi Shimbun


OSAKA--Summer spells fun for many, but not for air conditioner repair workers, who face incessant calls during the peak service period and often have to make repeated visits to fix a single problem. And since the average air conditioner has 2,000 or so components embedded inside, it's no easy task to figure out which parts they need to take with them when heading out on a repair call. To simplify life for those sweating to keep customers cool, leading air conditioner manufacturer Daikin Industries Ltd., based here, is relying on an artificial intelligence (AI) system to pick the parts. Few other firms in Japan are utilizing an AI system on such a large scale for repairs, Daikin officials said. As summer nears, the company's call center in Osaka's Chuo Ward begins being bombarded with calls from households and businesses requesting repairs.

Safe Contextual Bayesian Optimization for Sustainable Room Temperature PID Control Tuning

arXiv.org Machine Learning

We tune one of the most common heating, ventilation, and air conditioning (HVAC) control loops, namely the temperature control of a room. For economical and environmental reasons, it is of prime importance to optimize the performance of this system. Buildings account from 20 to 40% of a country energy consumption, and almost 50% of it comes from HVAC systems. Scenario projections predict a 30% decrease in heating consumption by 2050 due to efficiency increase. Advanced control techniques can improve performance; however, the proportional-integral-derivative (PID) control is typically used due to its simplicity and overall performance. We use Safe Contextual Bayesian Optimization to optimize the PID parameters without human intervention. We reduce costs by 32% compared to the current PID controller setting while assuring safety and comfort to people in the room. The results of this work have an immediate impact on the room control loop performances and its related commissioning costs. Furthermore, this successful attempt paves the way for further use at different levels of HVAC systems, with promising energy, operational, and commissioning costs savings, and it is a practical demonstration of the positive effects that Artificial Intelligence can have on environmental sustainability.


USATODAY - Tech Top Stories

Appliances, even large ones, are getting in on the action. But that doesn't mean you need to toss your old "dumb" stuff and buy brand new smart devices. Air conditioners are certainly one type of appliance that is heading down the smart path, but if you have a window or wall unit you already love (or can't afford to replace), you might consider turning your dumb unit into a smart A/C. With a few tweaks, you can convert the unit you have into a cooling solution you can turn on and off remotely with an app or through your favorite smart home ecosystems, such as Google Assistant or Alexa. A window or wall unit (or a portable A/C) works just fine when you manually turn it on with buttons, so why is it even necessary to make it smart?

HVAC Giant Trane Acquires EcoFactor's Home Energy Analytics Technology


EcoFactor is one of several startups with a cloud computing platform to manage and analyze data from smart thermostats and other home energy devices. But it also specializes in using that data to monitor and predict performance problems and impending failures of the air conditioners keeping houses cool. That kind of technology could have a lot of value to the companies that make heating, air conditioning and ventilation equipment -- enough to make it worth owning. On Tuesday, HVAC giant Trane announced it has acquired EcoFactor's energy analytics software for an undisclosed sum. Trane, a brand of Ingersoll Rand, plans to integrate EcoFactor's "unique artificial intelligence (AI) capabilities for energy efficiency and HVAC fault detection" into its existing Nexia home automation line.

Honeywell and Siemens launch automated truck unloaders that use AI to ferry packages at warehouses

Daily Mail - Science & tech

Robots are increasingly picking up the slack in package distribution centers. Honeywell and Siemens have unveiled new machines that are capable of autonomously ferrying packages from the tractor trailer to the fulfillment center with surprising accuracy, according to Bloomberg. It comes as consumers increasingly expect two-day or even same-day delivery, causing shipping companies to embrace automation as a solution to meet the spike in demand. Both Honeywell and Siemens' robot unloaders drive up to the back of a tractor trailer and use machine learning to identify packages. And, the companies say their machines work just as fast, if not faster, than human employees.

A supervised-learning-based strategy for optimal demand response of an HVAC System

arXiv.org Machine Learning

The large thermal capacity of buildings enables heating, ventilating, and air-conditioning (HVAC) systems to be exploited as demand response (DR) resources. Optimal DR of HVAC units is challenging, particularly for multi-zone buildings, because this requires detailed physics-based models of zonal temperature variations for HVAC system operation and building thermal conditions. This paper proposes a new strategy for optimal DR of an HVAC system in a multi-zone building, based on supervised learning (SL). Artificial neural networks (ANNs) are trained with data obtained under normal building operating conditions. The ANNs are replicated using piecewise linear equations, which are explicitly integrated into an optimal scheduling problem for price-based DR. The optimization problem is solved for various electricity prices and building thermal conditions. The solutions are further used to train a deep neural network (DNN) to directly determine the optimal DR schedule, referred to here as supervised-learning-aided meta-prediction (SLAMP). Case studies are performed using three different methods: explicit ANN replication (EAR), SLAMP, and physics-based modeling. The case study results verify the effectiveness of the proposed SL-based strategy, in terms of both practical applicability and computational time, while also ensuring the thermal comfort of occupants and cost-effective operation of the HVAC system.