The industrial business has come a long way in terms of innovation and sophistication. Component manufacturing is transforming with rising automation and quality standards. From raw material processing to superfinishing and testing, all are managed automatically. To maintain high-performance standards, the products are tested under heavy pressure and temperature conditions. These heavy machines include smaller load-bearing components, like bearings and guides, running under adverse physical conditions. The continuous exposure to heavy operating conditions demands regular maintenance activities. These maintenance activities relay the optimum performance of machines and maintain high-quality benchmarks.
Often coined as plant maintenance, industrial maintenance has a continuous process and cost associated with it. Maintaining assets to ensure peak performance with controlled costs can effectively gain a competitive edge in the marketplace. Maintenance strategies with sets KPIs and integrated automation can significantly improve overall operational efficiency and control excess costs.
Key industries that rely on industrial maintenance
The broad industries that heavily rely on industrial maintenance are the manufacturing industry, energy industry like oil, gas, mining, aviation industry, automotive industry and construction industry. These industries have industrial maintenance as a significant portion of capital expenditures and make up a big piece of the operational budget.
A holistic view of industrial maintenance stands on three supports. Asset failures maintenance requirements, internal resources – which includes the budget, skills, access to high-end tools and technology – and maintenance strategies on how different strategies work, their implementation costs and various pros & cons of using one over the other. The key is to maintain a mix of maintenance strategies and leverage different maintenance techniques as and when applicable.
A quick breakdown of different maintenance strategies that work best are:
Preventive maintenance: What if we can avoid problems before they happen? One of the most effective ways to ensure unplanned downtimes is to invest in continuous maintenance regularly and not wait for the machinery or component to break down. This approach seems like a regular ticket from the bank but can save loads of downtime punches and can also prevent critical components from failure.
Condition-based maintenance (CBM): Employing IoT and automation technologies, condition-based monitoring systems have proved to be the saviours of expensive assets. Sophisticated inside but simple-to-use and apply CBM technology determines the right time to go for maintenance. It employs non-destructive testing methods, like vibration analysis, ultrasonic testing and infrared testing to figure out whether the components should go for maintenance to keep up with peak performance.
Predictive maintenance: A step ahead of CBM, predictive maintenance uses collected data at the right points and combines it with historical asset maintenance data to create predictive algorithms. The data models are very accurate and can pinpoint when an asset is likely to fail. With these models at disposal, internal resources can be optimised to ensure absolutely no downtimes and maintain asset health.
Prescriptive maintenance: The most tech-fuelled asset maintenance strategy relies on Machine Learning and Artificial Intelligence to generate prescriptive algorithms. Apart from identifying likely to happen failures, these algorithms also find potential solutions.
How is Industry 4.0 revolutionising industrial maintenance?
The age of industry 4.0 is revolutionising industrial maintenance by doing much more with less expended resources. The rate at which technologies are advancing clearly distinguishes the fourth industrial revolution from its predecessors. Rising research and implementation in cyber-physical systems have led to a whole new era of industrial maintenance procedures and approaches. The global pandemic has altered organisational priorities to execute operations digitally. In order to meet consumer demand, digital and cyber technologies slushed into all facets of manufacturing and operations. Predictive maintenance technologies are revolutionising industrial maintenance.
All industries have discovered ways of operating efficiently with the help of automation and leveraging digital aids, which were never used before on such a massive scale. With digitisation, businesses could tweak internal resources with predictive ups and down in demands and realise a competitive edge in the marketplace. The faster the adoption of digital aids, the better the game rule was as the pandemic physically halted the world. It can be safely said that the pandemic has led businesses to explore the unexplored.
Key technology verticals
Some of the key technology components that enabled industrial maintenance transformation are cloud computing, industrial IoT, Big Data and cyber-physical systems, Artificial Intelligence, Machine Learning and mobile asset maintenance. The permutation and combination of these technologies has led to a revolutionary change in the industrial maintenance space and overall business outlook. Cloud computing has filled the gap between big corporates and boot-strapped start-up companies by providing a platform to try innovation models. With the help of cloud computing, information can flow to all stakeholders and be accessed from any corner of the world.
Industrial IoT has become one of the prime reasons for the paradigm shift from physical to digital maintenance strategies.
The technology is responsible for collecting and transmitting data to trigger notifications to check the equipment in question automatically. Cyber physical systems help in converting large amounts of data into useful information. It can create insightful patterns of physical asset depreciation and inefficiencies. The data generated at the right points support predictive and prescriptive maintenance planning.
With business expansions and rising demands fulfilment, it can be tough to source human intelligence at every point of the value chain. Sometimes, decision-making becomes important in the absence of human intelligence. Artificial Intelligence steps in the picture processing volumes of data and identifies patterns to execute a decision.
In order to deliver innovative products to customers, more time should be invested in finding creative solutions to complex problems. This area is still in a grey area for Artificial Intelligence. Meanwhile, if we can automate operational parts of the business, like documentation and regular check-ups of machines and other time-consuming tasks, more brains can be put into problem-solving. This is where the developments in Machine Learning can help. It is basically teaching machines/computers to perform tasks based on predefined rules and logic. When businesses integrate ML, operational productivity increases multi-fold.
Mobile asset closes the loop of intelligence and developments in industrial IoT. Mobile assets can quickly display maintenance activities through a user-based application. Data flows through the cloud, CPSs convert these data into information and the information generated can either be put through auto decision using AI or can take assistance from stakeholders, like field technicians or managers. The latter is possible with mobile applications wherein the technicians or managers can analyse what went wrong and quickly react with action steps. These triggers can flow to the cloud and into the machines.
Maintenance management is responsible for ensuring smooth working of industrial plant and helps in increasing overall productivity. Keeping the operating performance of machines at the peak and managing the dynamic demand of the market, these systems are inevitably necessary for efficient production. When the machines are running in optimum condition, the high quality of the manufactured product is maintained, thereby keeping quality standards high. Baseline readings are important to control the quality of operating systems as well as products. Condition monitoring tools use non-destructive tests to record historical data over time and nurture a baseline for problems like misalignment, loose electrical conditions, missing insulation, installation faults, leaking fittings or close couplings.
Automated lubrication systems are one of the new components to manage time-consuming lubrication. These automated systems add the lubricants at the right points and at the right amount to maintain a smooth operation and peak performance. There is another side to this story — the lubricant itself. Industrial maintenance is incomplete without the consumables used. The right quality of lubricants is as important as efficient lubrication systems. High-performance greases should be used in heavy machines operating under tough conditions of load and temperature. A lot of innovation is coming up in the tribology of lubricants and synthetic tweaking to increase product life and reduce maintenance downtimes. Together, efficient lubrication systems and high-quality lubricants pioneer the motion of industrial maintenance cycles.