The Internet of Things (IoT) is everywhere, and is much bigger than just a bunch of devices out there roaming around. The Internet of Things is in almost everyone’s pocket these days in the form of a smart phone, the car they drive, or the laptop or tablet computer they use for work or personal use. Industrial IoT is in almost every business in the world in the form of the systems and equipment that run manufacturing, production, operations, distribution, etc.
The promise of IoT is to “Liberate the Data” to create open integration, collaboration, and actionable insights with and from all these disparate, internet connected things. The term things is appropriate, because there is no way to put a more descriptive name or grouping on the myriad devices, equipment, vehicles, smart phones, sensors, and other data producing entities that can be used to create value through proper management and utilization of their data.
The majority of opportunity over the next decade will be implementing new solutions to integrate with existing industrial systems involved in scenarios such as manufacturing automation, monitoring and maintenance. Each industry presents unique opportunities and use cases, and at a technical level they can present unique devices and equipment to integrate with. An example is collecting data from manufacturing and processing automation systems (usually some form of programmable logic controller, or programmable automation controller) in order to process the data to calculate and report Overall Equipment Effectiveness (OEE) metrics and KPIs for insight into the manufacturing process health and efficiency.
Being able to access and capture high fidelity, high frequency data is the first and most crucial step in the Industrial IoT process, and is the step I think of as “Data Liberation”. Once you have reliable access to high quality, high frequency data, a world of possibilities opens up in the form of real-time data monitoring, action and insights, proactive and predictive analytics, dashboards, mobile apps, and more… The ability to perform machine learning/predictive analytics to improve process efficiency and performance or prevent breakage and/or work stoppage should prove to bring the biggest return on investment for a company investing in IoT. An example of this type of analytics in action would be collecting accelerometer sensor data in order to monitor vibrations from a motor, gears, or other equipment to determine when something might be loose or out of alignment. Using machine learning/predictive analytics it is possible to use the accelerometer data to determine the breaking point, and also to determine time based vibration thresholds in order to be able to schedule and perform preventative maintenance. Using “hot path” analytics techniques such as with Azure Streaming Analytics, it is possible to use the thresholds that were determined by the predictive analytics algorithms to create and send alert notifications once vibrations reach or exceed the threshold.
Once we have access to the things and their data, it opens up another realm of possibilities in the form of device, equipment, and process command and control. For example, using your mobile phone to start and stop the manufacturing production line if an alert is received. Using your tablet computer to increase the PH level of the water, or to open a valve further to increase water flow.
The cloud can be a great enabler of these goals for both industrial and consumer IoT. In reality, some companies are not ready to make the transition to the cloud, or have restrictions on sending operational data outside their company firewalls. The benefits of Industrial IoT can be realized without data ever leaving a companies private network. The goal of the Industry 4 Analytics blog site is to explore how to realize the goals of Industrial IoT in both cloud based, and on-premise scenarios, across a variety of industry use cases and technology stacks.
In upcoming blog posts we will explore the following areas of IoT:
– Cloud based options to get started with IoT such as Microsoft Azure and Amazon Web Services
– Edge device data collection using products such as OSISoft and Open Automation Software
– On-premise IoT implementations with products such as Kepserver EX and SQL Server
– Industry specific use cases such as Discreet Manufacturing, Waste Water Treatment and Recycling
– Integration of Operational and LoB data
– The device, equipment, and process control side of IoT
I look forward to diving into each of these areas in upcoming blogs!
Tim
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Latesha
I had no idea how to approach this be-fneorow I’m locked and loaded.
Raynes
Very valid, pithy, suictncc, and on point. WD.