Closing the maintenance skills gap
Published: 19 May, 2020
Maintenance processes are changing, and with them the skillsets needed by maintenance personnel. PWE consultant editor Andy Pye, tracks the march towards advanced technologies, with a nod towards some innovations, which would have been shown at the ill-fated 2020 Hanover Fair.
The shortage of engineers of all types in the United Kingdom is well documented, with age demographics and reduced immigration levels conspiring to create an impending crisis.
According to Bhavina Bharkhada, senior campaigns and policy manager at the manufacturing trade organisation Make UK (formerly EEF), the proportion of engineering vacancies considered hard to fill because of skills shortages is around 30%. “We know that three-quarters of manufacturers are struggling to recruit and when we look into why we see there are problems with the availability or quantity of applicants – with almost two-thirds saying they have an insufficient number of applicants,” says Bharkada. “But there is also a quality factor. To keep pace with demand we need 203,000 people with level 3+ engineering skills each year between now and 2024. And we are in competition, because 42% of theprojected demand for engineering skills comes from outside engineering.” Plant and maintenance engineers and technicians require a high levels of technical knowledge, problem-solving skills and initiative. They are responsible for ensuring complex systems don’t fail, that they are maintained to exacting safety standards and are kept at peak performance.
According to Efficient Planet, a badly maintained 10-year-old plant can cost more to maintain than a properly maintained 25-year-old facility. Maintenance techniques are evolving fast, as we move from traditional breakdown maintenance to proactive, predictive and preventive maintenance strategies:
Big Data is revolutionising the way manufacturers operate, and plant maintenance is no exception.
Augmented Reality (AR) and Virtual Reality (VR) are making their way into industrial environments.
Digital Twins (and recent advancements incloud and IoT) can create predictive analytics models that can predict when a failure or accident might happen.
Wearable Devices will be used to access use cases for training and automated animations of repair sequences.
Machine Learning (ML) enables computer systems to perform a specific task effectively without being explicitly programmed to perform the task.
In future, employers will need a multi-skilledworkforce – people who can take care of a wide range of problems that may arise in the day-to-day operation of manufacturing. Skilled workers will still be needed, but they will undertake different tasks and have different skill sets. In an interconnected, data-driven world, maintenance engineers are increasingly required to be software and hardware engineers, and even drone operators.
In this fast-changing environment, on-the-job training and Continuing Professional Development is more vital than ever. Many educational establishments, trade associations and equipment suppliers offer highly developed training programmes, many CPDcertified. Many members of the Society of Operational Engineers (SOE) are registered as Incorporated or Chartered Engineers. Members can obtain credits from course modules or by attending approved conferences and seminars.
The Fluke Industrial Maintenance programme aims to assist electromechanical technicians, and electricians become more effective at troubleshooting motors and motor drives. The company’s CPD-certified curriculum targets practising engineers, apprentices, students and teachers. Teaching materials are prepared by company experts and professional field troubleshooters. Upon completion of the training the technician should be able to quickly isolate a fault and facilitate a repair.
Autonomous maintenance
Designed to improve proactive, predictive and preventative maintenance in the factory, autonomous maintenance is a strategy whereby the whole manufacturing team take collective responsibility. Equipment operators work with the maintenance team to ensure the performance and health of their equipment. to be able to perform autonomous maintenance effectively.
Autonomous maintenance is a crucial pillar of TPM, developed in Japan to combine the concept of Total Quality Control. The idea was spawned by production teams who wanted to control and improve their overall equipment effectiveness (OEE) by reducing breakdowns, losses in speed and equipment deterioration. All operators must be trained to detect abnormalities, understand the equipment and be aware of the common causes of anomalies to identify the root of a problem when it occurs. The Japanese Institute of Plant Maintenance (JIPM) has defined a seven-step improvement process with three phases for companies to follow.
In-house or outsource?
Many plant managers face a tough decision - should they outsource their maintenance activity or hire an in-house team? Despite having in-house staff available to deal with problems as soon as they occur offering a significant advantage on response time, Outsourced contract maintenance is becoming increasingly prevalent.
Using an external maintenance partner with a specialist skill set, such as a robotics supplier, can also lower the time taken to repair, while having a contracted company avoids inhouse staff wages and benefits. As those of us currently experiencing working from home for the first time during the pandemic will have found, some managers can struggle with the lack of control that outsourcing brings as they are unable to directly manage, set task priorities and instruct the workforce.
Condition monitoring and data analysis in the cloud
Experts from Fraunhofer IPK expected to show at the Hanover Fair a system which integrates sensor technology into an internet platform that stores the full life cycle of one or more machine tools. The sensors issue a warning signal that the machine tool spindle should be replaced before damage occurs. The processing of the signal takes place directly on the sensor node. Consequently, the processor recognizes a fault by itself and can pass this information on.
Artificial Intelligence for Machine Tool Maintenance
Karlsruhe Institute of Technology (KIT) has developed a system for automated monitoring ball screw drives in machine tools. A camera integrated into the nut of the drive generates images that artificial intelligence continuously monitors for signs of wear, helping to reduce machine downtime.
Digital mobile maintenance
Manufacturing maintenance specialists are already using tablet computers to directly access key machinery digital equipment histories, documents and PLC programs on site. However, they can also import machine data directly from the PLC to the tablet. Harting’s MICA edge computer can communicate data between different machine proprietary operating systems and process the data in accordance. Consequently operators can arrange modification to PLC programming, optimising processes and reducing machine downtime. Mobile tablet solution offers direct access to all machine data on every piece of machinery
