The Internet of Things (IoT) is a hot topic. As Wired phrases it:
The Internet of Things revolves around increased machine-to-machine communication; it’s built on cloud computing and networks of data-gathering sensors; it’s mobile, virtual, and instantaneous connection; and they say it’s going to make everything in our lives from streetlights to seaports “smart.”
IoT is expected to revolutionize many industries from home automation, healthcare, driving and of course manufacturing. In this area applications include: asset tracking, production monitoring and improved ability to detect and predict errors for equipment maintenance.
The capabilities of IoT are more powerful than ever but control systems that are very similar in concept have been around for a long time. My first civilian job in Israel was working on a system that collected status information from telecomm equipment in order to identify failure. This involved polling the equipment for status and sorting the information to deliver problems to the next level and from there to a computer that could fix the problems or alert technicians.
I later learned that these types of systems are called SCADA for Supervisory Control and Data Acquisition, which means the ability to retrieve key pieces of information from remote devices in order to take action on these locations through the use of various controls and mechanisms.
This sounds a lot like IoT except the medium is now the internet and the sensors are more readily available. This can accelerate SCADA capabilities through several areas of progress that have changed so many other industries:
While the first three reasons have received a lot of attention and produced excitement about the IoT, advancement in analytics has received less attention as it related to IoT because it is much harder to define and standardize. Every system is different and the amount of data to contend with is huge and poses its only challenges and new processing requirements.
Here is an example we are working on with a client:
Problem: How do we detect and improve machine uptime in a production plant?
Our approach: includes implementation of machine learning and optimization techniques as follows:
Specify the relevant data to identify value before failure in real-time. An important aspect of this is to understand the necessary aggregation level n order to process the data effectively.
Identify the right combination of Machine Learning techniques that can use existing operating parameters to predict machine failure. Example techniques to explore include Linear Regression, Regression Trees, Random Forest, Bagging, Boosting, K-nearest neighbor, etc.
Define the problematic conditions and possible corrective actions
Implement learning algorithms that will improve as more data is fed
Automatically decide on the best course of action using optimization
The exciting opportunities provided by combining IoT with advanced analytics in this example is that, rather than simply reacting to problems that have already occurred, we can monitor continuously and predict problems before they happen. This then allows us to decide the best course of action (using optimization) to mitigate or avoid the problem entirely. Also, because these algorithms can leverage large data sets, they can improve over time as more data is fed to the algorithms. A virtuous cycle is created that enables continual improvement.
The problems companies face with equipment such as maintenance and visibility are not new. But the combination of new sensor technology, computing power and the internet provide a shift in the way we approach these challenges - as the Internet of Things. A key part of this are new analytics techniques that will enable the 'smart' portion of the vision.
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