May 2020

12 | Plant & Works Engineering www.pwemag.co.uk May 2020 Maintenance Matters Focus on: Training T he 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 the 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. Closing the maintenance skills gap projected 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 in cloud 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-skilled 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. 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 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.