Today’s successful maintenance engineers gained proficiency through years of formal and on-the-job learning combined with mentoring and coaching from senior experts. The resulting skills and expertise are broad and deep. However, even the most experienced professionals can’t anticipate and consider all the data and eventualities that matter when it comes to your and your customers’ industrial assets. How do you help them succeed while also increasing the value of your machine products and services?
In discussions and publications about predictive maintenance and asset management, data analytics and machine learning draw much of the attention. Connected sensors on machinery within the internet of things (IoT) provide visibility of the realtime status and the subtle changes in a machine’s operation and performance. This data can be combined with environmental data and data from other sources, such as computerized maintenance management (CMMS) or ERP systems, in a machine learning and data analytics solution.
Doing so enables you to gain insight into the functioning and behavior of your machines or production lines. This insight can enable asset managers and maintenance engineers to perform predictive maintenance that keeps a machine’s performance, uptime, and output quality at optimal levels. New data and further insight result in steady improvements in the timeliness and effectiveness of predictive maintenance.
Machine learning and data analytics are popular concepts that many larger manufacturing and production companies explore, and sound algorithms and proven solutions already support proactive maintenance. However, it’s easy to forget that the discipline of data-driven maintenance is still quite young. IoT and other data is not always of good quality, many software solutions are not ready for wide adoption, and creating reliable predictive models is still a challenge.
In addition, to many business people data analytics and machine learning appear reductionist because they exclusively emphasize quantifiable values. Without expert assistance to translate data-driven insight into practical terms, they may not be able to make the connection to predictive maintenance and required process or business-model changes. In smaller companies, limited budgets and lacking access to analytical expertise may slow the pursuit of predictive maintenance.
Data analytics and machine learning are essential components of what a recent PwC whitepaper calls Predictive Maintenance 4.0. It transcends proactive maintenance based on realtime condition-monitoring by combining and analyzing data from multiple sources to help companies anticipate and address failures that are otherwise inexplicable and unforeseeable. In addition to advanced software tools, Predictive Maintenance 4.0 calls for the skills of data analysts and reliability engineers to build the bridge from data findings to the business stakeholders.
Turnover and an aging workforce in industrial maintenance make it difficult for companies to create the digital cultures with the skills and capabilities that enable Predictive Maintenance 4.0. It can be challenging to find the tools and resources to ensure the participation of the most qualified maintenance experts in transforming the operation.
Experienced maintenance engineers draw on and combine information from many different sources to ensure timely, effective maintenance and optimal machine productivity. They apply insight gained from learning about machine conditions, machine specifications, operator experiences, production processes, and other areas of knowledge together with sophisticated problem-solving, communication, and management skills.
These professionals will be reluctant to embrace data analytics and machine learning solutions that do not have the explanatory power to make their work easier. That capability is critical when your most experienced people are about to retire, the complexity of your machinery is unprecedented, and the customer impacts as well as the financial and operational costs of poor performance and unplanned downtime can be staggering.
One way to bring explanatory and predictive power to maintenance engineers and also ensure that your key people will adopt an automated, digital maintenance solution is to make it both highly functional as well as connected. Decision-making and planning can become more meaningful when you contextualize data analytics and machine learning with current operational, engineering, R&D, and maintenance processes.
Without a tool to track and provide transparence of machine histories, KPIs, process and production trends, and other aspects of your operation, your maintenance engineers and asset managers will soon be overwhelmed. There is simply too much to remember and review, especially when you collaborate with customers on new engineering designs and the evolution of your products takes off in several directions.
Based on experiences and requests from the maintenance engineers and asset managers in companies that manufacture and use complex machinery, we designed innius to enable them to collaborate and make full use of their expertise. Using the solution to perform realtime condition monitoring of machines and industrial assets helps you implement preventive maintenance now and gradually transition to predictive maintenance.
innius also gives you a way to improve your customer responsiveness and service levels with ongoing improvements of your engineering design and machine performance, foregoing the cost and effort of a data analytics and machine learning initiative. You can apply the solution’s standard KPIs or create your own, and easily integrate it with other systems and data sources.
I welcome your questions and feedback; please contact me.
innius: a machine user’s business case
