Machine learning overcomes the limitations of human operators and traditional data analysis to provide practical insight
Machine learning originally was an extremely ambitious, almost futuristic concept: a kind of artificial intelligence that furnishes a software program with the ability to process information in such a way that it can learn without being re-programmed, and change its activities in response to new findings from data. Today, machine learning is an important element in many companies’ digital transformation. High-potential use cases for machine learning include fraud detection, financial trading, data security, and others.
An industrial usage scenario for machine learning
In manufacturing and other industrial environments, machine learning can help engineers and production managers understand how well their machinery is working, which potential issues might affect production, and how to improve uptime, performance, or output quality. To accomplish this, sophisticated analytical and predictive tools are applied to the continuous sensor data streams coming from machines connected to the internet of things (IoT). By making use of advanced, automated analytics to identify patterns and trends in machine behavior across all the data at hand from all relevant sources, machine learning overcomes the dependency on human-powered analytics that can easily become a bottleneck when faced with large data masses.
While it makes sense that such factors as temperature and humidity play a role in how well and how consistently complex machinery functions, for much of industrial history insights regarding machine environments were not systematic. Companies depended on their most experienced professionals, who would often record and analyze their limited data findings in form of spreadsheets. Machine learning, in contrast, can take into account all data streams from the sensors of a machine connected to the IoT. It can also weigh them contextually and in consideration of the changes in machine environments.
Machine intelligence to benefit multiple business roles
When you imagine the practical applications of machine learning, remember that IoT data findings can help both the people who are responsible for operations as well as those who need to keep machines running and productive. Connected sensors document changing machine performance levels as well as quality variances in a machine’s output along with environmental data related to vibration, pressure, temperature, humidity, and other conditions. The right analytics helps people in a variety of roles—in companies that make, use, and service machinery—transform data into understandable information, which they can use to make machines more productive and durable. Production managers and planners can, for instance, rely on analysis of machine sensor data to assess such performance indicators as Overall Equipment Effectiveness (OEE), a compound which includes the quality, performance, and availability of a machine or a line of machines. At the same time, their colleagues in maintenance closely watch especially the availability component value of OEE to ensure uncompromised, continuous operation of their machines.
Several companies using innius, our machine intelligence solution, take advantage of its out-of-the-box KPIs, which include OEE, for both operational and maintenance purposes. When you can add automation to machine learning and eliminate most of the low-level chores associated with the gathering, analysis, and presentation of data, it becomes easy to assess your OEE and other metrics more frequently—for example, every day or at the end of every production shift instead of once a week or less often. The results are actionable, drawing your attention to concerns and improvement opportunities identified by the solution. You can then take the initiative in a prompt, close to real-time manner when machines diverge from their quality parameters or appear to be heading for a breakdown anywhere in their parts and components.