In manufacturing facilities, unplanned outages force continuously operating processes to go through shutdown and start-up procedures. It's under these conditions that serious incidents are most likely to occur. Ideally, manufacturers should to be able to anticipate equipment failures to prevent these outages. Previously, monitoring a wide range of pumps, drives, motors, steam traps, relief valves, and other equipment was implausible because of high operating costs. However, the increasing availability of inexpensive, wireless sensors now let's facilities measure safety, regulatory compliance, equipment reliability and energy efficiency without breaking the bank.
Traditionally, the cost of adding a wired sensor to gather data was very high. This has limited many manufacturers to only collecting data from critical applications. Today's innovative sensor technology, including wireless and non-invasive sensors, has made installation cost-effective and maintenance straight-forward. The embedded technology inside today's sensors now allows them to provide direct actionable information.
At the 2013 Emerson Exchange, the concept of pervasive sensing was introduced to industry to explain how smart sensors gather actionable information to improve operations and maintenance.
Using the example of tire motoring, Emerson illustrated how technology is now being used to accomplish tasks that were previously carried out manually. Today, wireless sensors in each tire constantly monitor the pressure and provide automated warnings and specific maintenance instructions to the driver on the car dashboard.
A pervasive sensor is a robust sensor with no requirement for maintenance or calibration once installed. It's typically a clamp-on sensor or externally mounted. Pervasive sensing devices include vibration sensing of rotating equipment, ultrasonic leak detectors, wireless steam trap monitors, corrosion and erosion detection technology, as well as wireless bolt-on surface temperature probes. Using multiple sensors to gather information makes it possible to detect and respond to hazards before they become a danger to people and equipment. The technology can also predict failures, reduce downtime, avoid environmental issues and identify potential security threats.
In manufacturing, pervasive sensing helps in both process-critical and business-critical applications. Process-critical refers to process control and process safety applications. This data requires immediate response to prevent off-spec product or even plant shutdown. Most of the equipment involved is already automated, since sensors generally connect to an existing process control system.
One drawback to keep in mind is the possibility of information overload, as most of the information gathered by pervasive sensors acts to keep plant operators informed, rather than close process loops. If design engineers don't apply design principles that the workforce can manage, there may be so many alerts and alarms that the operators cannot keep up with them.
Business critical refers to site safety, reliability and energy efficiency. While process safety is really about containment, site safety is typically about worker safety or gas detection. Business critical data requires a timely response, rather than the immediate response needed by process critical data. Failure to act on it can lead to outcomes such as plant slowdown or increased energy usage.
Pervasive sensing provides a better understanding of the condition of assets and the process performance. This allows for a more predictive, risk managed approach to intervention, ensuring that the correct equipment or replacement parts are available when required. The move to risk-based management together with the emerging need to focus on collection of mobile assets rather than a fixed site will drive the future demand for pervasive sensing.
For further information, view website: www: www.euautomation.com
Graph technology and machine learning
The theory of six degrees of separation, first proposed in 1929, suggested that every individual in the world was connected to anyone else in no more than five links. Today, social networking tools and graph technology can accurately map and extract valuable insights from the relationships between various entities in a network. Networks can also be analysed by machine learning, a technique in which a computer can adapt its own algorithms. Here, Jonathan Wilkins, marketing director at EU Automation looks at how both can be used to make sense of big data in the manufacturing industry.
Modern manufacturing equipment has been advancing rapidly; plants are filled with sensors to monitor equipment performance. The number of sensors that allow devices to connect to the internet is growing and so too is the volume and complexity of data available to plant managers. The collection, storage and analysis of this data is vital in unlocking the benefits big data can provide.
Traditionally, data has been stored in table-structured relational databases, but development in this field has led to the introduction of the next generation of relational databases, graph databases, a type of NoSQL database. In a graph database, information is stored and represented with nodes, edges and properties. Nodes represent individual entities, edges are lines that connect nodes to each other and properties represent information relevant to the nodes. Unlike relational databases, which form a square structure, graph databases are much more flexible.
Graph databases can be used to quickly access information and identify trends in large data sets, such as supply chain patterns, logistics and new business leads. The system is naturally adaptive, allowing new nodes to be easily added. The analysis can be done in real time to address problems in manufacturing.
Machine learning is a concept that has been around for many decades. In machine learning the computer doesn't rely on rule-based programming, rather the algorithms can adapt and learn from the data. This means that manufacturers using this software don't need to rely on the time and expense of dedicated data analysts to find patterns and make predictions. Companies like Amazon have also used cloud based machine learning to make warehouse logistics more efficient by being able to quickly and seamlessly adapt to changes in inventory demand at peak times and during seasonal highs and lows.
Machine learning can incorporate hundreds of causes, effects and non linear responses. This model can adapt itself over time to continually improve the quality of predictions. Machine learning can be combined with graph databases to gain valuable insights into processes. Whether it's condition monitoring or predictive maintenance of a process plant, demand forecasting in automotive manufacturing or digital twinning -- a type of virtualisation -- machine learning facilitates better decision making in an increasingly complex business environment.
Machine learning is commonly used for predictive analytics, which can give insight not only into customer intentions, but also into the state of machines on the factory floor. Information analysed from the sensors can relay any potential suboptimal performance that may lead to unplanned down time if left unaddressed. This leaves plant managers time to order replacement parts, such as an obsolete or refurbished part from EU Automation, or perform other necessary maintenance to prevent system failure.
Machine learning is particularly useful in largely automated systems, where equipment is required to make its own decisions. The continuous learning process makes data more reliable, analysis techniques more repeatable and ultimately improves the human input into any system. Aside from predictive analytics it can also be applied to optical part sorting, failure detection, analysis and product testing.
Although machine learning and graph technology both offer a powerful way of analysing the ever increasing volume of data available to us, much of the technological potential is yet to be realised. To gain the most valuable insights, it's important that business leaders embrace a thoroughly modern form of analysis. In doing so, it may come as less of a surprise that competitive advantage is less than a mere six degrees of separation away.
European Automation in profile
European Automation stocks and sells new, used, refurbished and obsolete industrial automation spares. Its global network of preferred partner warehouses, and wholly owned distribution centres, enables it to offer a unique service within the automation industry, spanning the entire globe. It provides worldwide express delivery on all products meaning it can supply any part, to any destination, at very short notice.
For further information, view website: www: www.euautomation.com