Industrial IoT is becoming an emerging point of conversation, particularly within the industrial sector. It’s not just manufacturers that are realizing the true potential of having your entire process be inter connected. In this article our focus is using cloud computing to optimize your IIoT process. Predicative analysis is using a machine learning algorithm to ensure you stay on top of maximizing your results. Let’s explore the many facets of industrial IoT.
Industrial IoT bridges the gap between legacy industrial equipment and infrastructure and new technologies such as machine learning, cloud, mobile, and edge computing. Customers use IIoT applications for predictive quality and maintenance and to remotely monitor their operations from anywhere. IoT services enable industrial companies across industries such as mining, energy and utilities, manufacturing, commercial agriculture, and oil and gas to reason on top of operational data and improve productivity and efficiency.
Predictive quality analytics extracts actionable insights from industrial data sources such as manufacturing equipment, environmental conditions, and human observations. The goal of predictive quality analytics is to determine actions such as adjusting machine settings or using different sources of raw materials that will improve the quality of the factory output. Using IoT, industrial manufacturers can build predictive quality models which help them build better products. Higher quality products increase customer satisfaction and reduce product recalls.
Predictive maintenance analytics captures the state of industrial equipment so you can identify potential breakdowns before they impact production. With IoT, you can continuously monitor and infer equipment status, health, and performance to detect issues in real-time. When organizations use predictive maintenance analytics, equipment lasts longer, worker safety increases, and the supply chain is optimized.
Asset condition monitoring captures the state of your machines and equipment so you can understand how the asset is performing in the field or on the factory floor. Typically, data such as temperature, vibration, and error codes indicate if equipment usage is optimal but it’s hard to capture manually since technicians need to physically inspect machines. With IoT, you can capture all IoT data and monitor performance. With increased visibility, you can maximize asset utilization and fully exploit your investment.
The interest in machine learning for industrial and manufacturing use cases on the edge is growing. Manufacturers need to know when a machine is about to fail so they can better plan for maintenance. For example, as a manufacturer, you might have a machine that is sensitive to various temperature, velocity, or pressure changes. When these changes occur, they might indicate a failure.
Prediction, sometimes referred to as inference, requires machine-learning (ML) models based on large amounts of data for each component of the system. The model is based on a specified algorithm that represents the relationships between the values in the training data. You use these ML models to evaluate new data from the manufacturing system in near real-time. A predicted failure exists when the evaluation of the new data with the ML model indicates there is a statistical match with a piece of equipment in the system.
Typically, an ML model is built for each type of machine or sub-process using its unique data and features. This leads to an expansive set of ML models that represents each of the critical machines in the manufacturing process and different types of predictions desired. Although the ML model supports inference of new data sent to the Cloud, you can also perform the inference on premises, where latency is much lower. This results in a more real-time evaluation of the data. Performing local inference also saves costs related to the transfer of what could be massive amounts of data to the cloud.
You need to build and train ML models before you start maintenance predictions. Start by collecting supporting data for the ML problem that you are trying to solve and temporarily send it to an IoT Core. This data should be from the machine or system associated with each ML model. A dedicated connection between the on-premises location of the machines and IoT Core supports high-volume data rates. Depending on the volume of data you are sending to the cloud, you might need to stagger the data collection for your machines.
References: Much of this article has been derived from an AWS expert article on Industrial IoT.