There is no shortage of practical commercialized applications around machine learning, AI and blockchain for IoT throughout enterprise and government organizations. Where we have seen the most value across enterprise and government is within prescriptive maintenance. The science of prescriptive maintenance is finally on the cusp of a major transformation with IoT, edge computing and machine learning all poised to accelerate in an era of 5G, quantum computing and innovation in low-power, high-performance processing applications.
It’s critical for companies and government entities to understand the maturity curve of maintenance so they can determine where their operations currently are, where they want to be and where they will get the most return for their investments in technology and processes. They need to explore how to evolve their maintenance programs with future-proof technologies or at least technologies that are not suddenly outdated in the next few years. Prescriptive maintenance is emerging as the next generation of maintenance strategies and will most certainly be a major part of the fourth Industrial Revolution.
What is meant by prescriptive maintenance? The term prescriptive maintenance is derived from the principle of prescriptive analytics. This concept is a step past prescriptive maintenance and it not only supplies the possible outcomes in a situation, but it also gives the best way to approach the maintenance requirements based on analysis of those outcomes. Prescriptive maintenance techniques are designed to help determine the condition of in-service equipment in order to estimate when maintenance should be performed.
Most prescriptive maintenance is performed while equipment is operating normally to minimize disruption of everyday operations. This maintenance strategy uses the principles of Statistical Process Control to determine when maintenance tasks will be needed in the future. The aim of prescriptive maintenance is first to predict when equipment failure might occur, and second to prevent the occurrence of the failure by performing maintenance. Monitoring for future failure allows maintenance to be planned before the failure occurs.
In prescriptive maintenance, a number of tools and techniques monitor the condition of machines and equipment to predict when problems are going to occur by identifying the symptoms of wear and other failures. Prescriptive maintenance is also a philosophy that uses the equipment’s operating condition to make data-driven decisions to improve quality, productivity and profitability. The difference between preventive and prescriptive maintenance is that preventive maintenance tasks are completed when the machines are shut down and prescriptive maintenance activities are carried out as the machines are running in their normal production modes.
Prescriptive maintenance allows government or commercial entities to lower maintenance costs, extend equipment life, reduce downtime and improve production quality by addressing problems before they cause equipment failures. The more high-quality data fed into the prescriptive model, the better its accuracy. Some examples where prescriptive maintenance can be implemented for enterprise and government include the tying together of live monitoring equipment with historical failures and maintenance logs, along with the spare parts refurbishment inventory and maintenance ticketing systems that automate the process of understanding signals that lead up to failure. Algorithmically, it can then have the system check if there is a spare part in inventory and then process the work order for the maintenance event to happen all in a fluid process.
Anyone can advertise these tools. But note that artificial intelligence, machine learning and blockchain services are only part of the process of building, training and deploying coherent models into production systems. When bringing an AI and deep learning solution to a problem, ensure that experience is represented in all aspects of the technology stack.
Any individual can operate the machine; it requires additional knowledge to manage the system. It is critical to determine ways data can be used to configure and trigger machines, prove authenticity or produce any type of output intended to get a business closer to its goals. Also, work to define a problem well before its solution to ensure that the right data gets to the right person or system at the right time.
All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.