In order to ensure an efficient and continuous production cycle, as required by the growing demands of the global market, proper maintenance of industrial machinery is crucial. For a long time, machinery maintenance was perceived by manufacturers as an obligation and an expense that did not create value. But today maintenance can be transformed into a competitive advantage thanks to the Industrial IoT (Internet of Things) and artificial intelligence (AI) – the technologies at the base of industry 4.0.
The innovation and digital transformation of companies, with the consequent availability of more data on the state of physical assets, allows for predictive maintenance. Based on the analysis of data collected from machinery, it is possible to predict and prevent failures, malfunctions, and unplanned downtime, to avoid downtime, minimize the impact of maintenance costs and ensure efficient management of the entire process.
According to research published by IndustryWeek, unplanned downtime costs the manufacturing sector $50 billion a year, not counting the reputational risks for companies nit delivering product. In 42% of cases, downtime is due to unexpected equipment failure. That is why maintenance is so important.
Despite these alarming figures, many entrepreneurs and companies are not able to calculate how much an hour of downtime really costs the company or quantify the damage that could be suffered. However, this can be overcome with predictive maintenance. The ability to monitor functional parameters and maximize the performance of one’s machinery offered by IIoT and AI technologies, through the collection and processing of data from the field, allows prediction of possible problems and schedule maintenance without incurring in downtime – saving on related costs. Therefore, we will see how and why industrial machinery maintenance is more efficient with IIoT and AI.
Industrial machinery maintenance
In the era of Industry 4.0 and smart factories, when talking about industrial machinery maintenance, it is preferable to refer to predictive maintenance. Every organization has assets – machinery, production plants – that constitute the physical infrastructure of the company, whose maintenance in optimal conditions is the prerequisite for successfully managing the production cycle. Today, thanks to digitization, these physical assets are able to “talk” to so-called intangible assets of the company, i.e. data, algorithms, software, cloud services, IoT technologies, artificial intelligence. It is precisely in this dialogue that the conditions are created to apply the paradigm of predictive maintenance.
The basis of predictive maintenance is the huge amount of data collected in the plant – from sensors and IoT technologies within the machine – easily converted into information about the state of physical assets that allow proactive action in case of problems. By predicting when a failure or malfunction could occur in a plant, for example, it is possible to adopt appropriate countermeasures, such as ordering the necessary spare parts for repair or replacing worn components – even before the breakdown occurs and the regular operation of the plant is interrupted. The goal is to progressively improve the maintenance of industrial machinery, reducing as much as possible machine downtime caused by sudden and unexpected breakdowns, which are decidedly costly for the company.
IIoT and AI, an inseparable combination for the maintenance of industrial machinery
For predictive maintenance to be feasible, it requires IIoT and AI technologies that provide real-time information on the conditions of each machinery and its components. Specifically, it is necessary to ensure the interconnection between physical assets and IoT platforms, achieved by equipping each machine with IoT sensors whose data is digitized and connected to a network, whether wired like Ethernet or wireless like Wi-Fi. With real time data aggregation, we can process and correlate data throughout the plant.
From the analysis of this data, using the most sophisticated big data analysis techniques, an IIoT platform, such as CLEA from SECO, for managing corporate assets with AI can evaluate the operational effectiveness of plant machinery. Analyzing the productivity of each asset in relation to its history and to each other, anomalies can be signaled to operators in real-time, thanks to the presence of appropriate alert and reporting systems. In this way, intelligent maintenance activities (smart maintenance) are carried out, often remotely.
The advantages of intelligent maintenance of industrial machinery
Among the numerous advantages of intelligent maintenance of industrial machinery, the first is increased productive efficiency, which is achieved by reducing and in some cases eliminating downtime and breakdowns. This results in a reduction in maintenance costs, thanks to the optimization of the number of interventions, and those related instead to the loss of economic value generated by the unplanned interruption of production. This also leads to an extension of the useful life of the machinery, due to the limitation of major breakdowns, and a reduction in costs related to the purchase of spare parts. Furthermore, by constantly monitoring production through data processing, it is also possible to predict and plan future company activities. Predictive maintenance thus allows for reducing downtime, maintaining high plant efficiency, and controlling the level of quality of the products produced.
The CLEA Smart HMI solution from SECO monitors the states of machinery with AI models, scheduling maintenance interventions on machinery before they break down. The connection to the cloud allows for full use of the potential of predictive IA apps: whatever the problem, CLEA can provide an app to prevent breakdowns and detect anomalies. With CLEA Smart HMI, in addition, the machinery can be accompanied by a premium/freemium maintenance service that includes optimized support, application of predictive analysis models, and quick provision of spare parts for a recurring fee. For industrial machinery manufacturers, choosing the CLEA Smart HMI solution means adopting a new business model, oriented towards the provision of innovative and high-value-added services for remote support and predictive assistance.