Launch of predictive maintenance solutions continues to gain massive momentum in the ICT industry
June 2, 2022 1:40 pm
Increasing use of innovative technologies such as advanced neural networks and artificial intelligence to deduce and raise necessary alerts.
Predictive maintenance has gone through transformational changes over the last couple of years, owing to the surge in need to propel asset uptime and reduce maintenance costs of the machineries. In the earlier days, companies used conventional systems, and hence they had to depend on the historical data about previous equipment breakdowns and performances. Predictive maintenance is an ideal technique adopted by numerous players of the market to effectively monitor the performance and condition of the equipment so as to appreciably minimize the risk of equipment failure under normal or uniform working conditions. On the other hand, modern predictive maintenance solutions repeatedly monitor the behaviour and performance of the equipment so as to collect the vital data in real time.
Increasing use of innovative technologies such as advanced neural networks and artificial intelligence (AI) to deduce and raise necessary alerts if any sort of potential equipment failure is bound to happen. In addition, various organizations across the globe are leveraging on various machine learning technologies for not only increasing the speed of processes over conventional tools, but also for providing enhanced accuracy to monitor crucial data about the equipment. These factors drive the demand for predictive maintenance in the coming years.
Apart from that, there has been a significant surge in the awareness about the importance of predictive maintenance among industrial customers. Predictive maintenance is extensively used in the industrial manufacturing sectors such as the food and beverage and oil & gas industry. They are also widely used in other industries including healthcare, consumer goods, transportation, and others. Thus, organizations tend to adopt utterly effective predictive maintenance solutions to notable make a prediction 20 times faster than conventional threshold-based monitoring systems.
Besides, downtime can be quite expensive, and hence can cost a lot of money for organizations dealing in heavy industries such as oil & gas. These factors are further expected to boost the demand for predictive maintenance in various countries around the world in the forthcoming years.
Moreover, increase in investments in predictive maintenance, surge in need to prolong the lifespan of ageing industrial equipment, rise in concerns over data privacy issues, and increase in adoption and integration of industrial internet of things (IIoT) are predicted to create tremendous opportunities for the growth of the global predictive maintenance market. According to the report published by Allied Market Research, the global predictive maintenance market is anticipated to reach $31.96 billion by 2027, growing at a CAGR of 28.8% from 2020 to 2027.
Numerous players of the market across the globe are launching innovative predictive maintenance solutions to effectively cater to the needs and wants of customers. For instance, Aizon, a California-based AI software provider that is engaged in revolutionizing manufacturing operations in the biotechnology pharmaceutical industries with the application of artificial intelligence, advanced analytics, and other smart factory technologies, launched a distinctive predictive maintenance solution called Aizon Asset Health for seamless pharmaceutical manufacturing.
Aizon has applied multivariate analysis, advanced analytics, and artificial intelligence to increase the efficiency of the Aizon Asset Health application. This application essentially invigorates the minimization of unpremeditated and spontaneous downtime with the help of condition-based maintenance that remarkably surpasses the modern industry standard.
In addition, the Aizon Asset Health efficiently analyses the condition of the crucial assets in real time, provides an impeccable historical maintenance analysis, determines potential problems, and provides necessary maintenance recommendations and solutions to keep the machinery up and running. Thus, it ensures full asset integrity, complying with optimal facility conditions such as vibrations, temperature, and humidity, apart from tracking the energy consumption of the asset and its carbon dioxide emissions. Thus, Aizon has unlocked the maximum potential of smart pharma manufacturing whilst improving asset reliability and performance for organizations.
The trend of launching creative predictive maintenance solutions by prominent players of the market continues to gain massive momentum in the information and communications technology (ICT) industry. For instance, TeamViewer, a dominant tech-based company engaged in providing secure remote connectivity solutions, announced the launch of its new AI-supported software module called ML-Trainer.
It is capable of learning and determining specific patterns and supplies machine learning algorithm with data. With the help of ML-Trainer, both false alarms and downtimes can be appreciably minimized as artificial intelligence is persistently learning. The TeamViewer predictive maintenance module can seamlessly be affiliated with the current TeamViewer IoT environments.
The algorithm can efficiently make use of the sample data sets that is intrinsically created for this module of various machine types including pumps and wind turbines, and hence it only needs to learn about various attributes of the respective machine. The Vice President of IoT at TeamViewer, Lukas Baur, mentioned that the maintenance department accounts for nearly 60 percent of the operational expenditure, and hence their major focus is towards minimizing this expenditure with help of an AI-based analysis of the device data. Thus, various players of the market are making proper use of innovative technologies to effectively make machinery maintenance seamless and cost-effective.