Industrial robot companies investing in the cloud and Big Data
In 2014, Fanuc partnered with Cisco on a 12-month zero downtime (ZDT) pilot project with General Motors. Fanuc is now moving forward to connect all its manufacturing robots. The system proactively detects and informs of a potential equipment or process problem before unexpected downtime occurs. This lets Fanuc and its customers schedule and perform maintenance during a planned outage window, so operations aren’t disrupted.
Fanuc robots contain sensors that constantly gather data on temperature, cycles, machine operator activities, and other metrics. This data is then dynamically analyzed to predict wear on parts such as bearings or transducers. An analytics engine captures out-of-range exceptions and predicts maintenance needs. The cloud app alerts Fanuc service personnel and its manufacturing customer about the need for service and replacement part(s). The part(s) is automatically shipped to arrive at the factory in time for the next scheduled planned maintenance window.
This kind of proactive, planned maintenance can unleash dramatic savings and is different from present-day equipment- and part-life maintenance and service programs. General Motors estimated unplanned downtime costs thousands per minute and Fanuc hopes to help GM save an estimated $40 million in downtime.
Kuka and Huawei
Kuka is working with Huawei, the Chinese phone maker and communications service company, on a similar project to develop a global 5G network enabling the connection of Kuka robots across many factories. The companies say they plan to integrate artificial intelligence and deep learning into the system to help manufacturing businesses remain agile and drive growth. In a March 2016 agreement, Huawei and Kuka said they will collaborate in the areas of cloud computing, big data, mobile technology, and industrial robots.
ABB and Microsoft
ABB offers an optional software package to ABB robot owners called Connected Services. This is a 24/7/365 monitoring service for their robots but administered by the client; it is not an ABB-wide effort to monitor and learn from their whole network of robots around the world. However, that learning capability is available and may be available in the future as can be seen by ABB’s Connected Service offered by ABB’s power business, where they are working together with Microsoft on a cloud-based e-mobility charging platform for power stations for electronic vehicle recharging. This will include user recognition, accepting payments, and data transfer of streaming data for system stability and monitoring.
Robots learning to adapt and improve
Aethon, a mobile robotics company focusing on the hospital industry, started their Cloud Command Center in 2013, which allows them to remotely monitor, support, and even control the autonomous mobile robots installed at their customer’s locations. Aethon staffs the command center 24/7/365 and has over 400 of their Tug Robots in 140 locations online and in constant communication with the command center. Occasionally, a Tug finds itself in a situation where it needs help. Rather than rely on customer personnel to address this, the command center takes over and manages the situation.
Algorithms monitor the status of each Tug in real-time and if the algorithms detect a Tug might need help an alert is sent to an on-duty support staff member who, using a secure VPN connection, can connect to the Tug’s on board sensors to assess the situation. In the simplest of solutions, the operator can drive the device out of the situation. Whatever the case, the command system updates with the solution and the criteria that precipitated the situation. Thus the system is not only handling problems, it is learning how to anticipate those very same situations in the future and proactively prevent them.
Black box deep learning
Kuka, ABB and Fanuc—along with most robot makers—are late to the A.I. and deep learning party, but are still welcomed for what they can provide.
The most visible of the efforts in deep learning are in Silicon Valley, which has seen widespread start-ups in A.I. research. Car companies such as Baidu, Alibaba and, most recently, Toyota’s $1 billion investment in establishing the SV Toyota Research Institute.
Apple, Google and Facebook have led investment in more advanced uses, but practical deep learning systems such as Aethon’s, Fanuc’s and Kuka’s are also becoming prevalent in the industrial sector.
The black box concept—the storing of streamed sensor data for analysis and learning—is a valuable tool in air safety and may soon become a mainstay in autonomously driven transportation, mobile robots and robotics in general. That data, and super-fast computer processing, are enabling deep learning engines to find and build patterns that can make the devices safer, more productive, and more cost-effective.
Frank Tobe is the owner and publisher of The Robot Report. After selling his business and retiring from 25-plus years in computer direct marketing and materials, consulting to the Democratic National Committee, as well as major presidential, senatorial, congressional, mayoral campaigns and initiatives all across the U.S., Canada and internationally, he has energetically pursued a new career in researching and investing in robotics. This article originally appeared on The Robot Report. The Robot Report is a CFE Media content partner. Edited by Chris Vavra, production editor, CFE Media, firstname.lastname@example.org.
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