Miras condition monitoring is an effective tool to detect of mechanical machine damages in any platform
The availability of machines and plants is a basic requirement for productivity. In order to minimize unplanned downtimes, it is vital for any plant manager to detect the sources of error at an early stage. Subsequently, maintenance work can be scheduled in order to avoid interrupting production time.
This is precisely where Miras Condition Monitoring Systems come in. They continuously monitor the condition of mechanical components in your machinery through your sensors data and all over the plant. As a result, any changes resulting from wear or other damage based on the documented trend histories can be detected at an early stage using machine leaning algorithm and issues resolved before they lead to plant downtime.
Longer life cycle
Record, store and analyse mechanical sensors data
Supporting open standards
Effective spare parts maintenance
Predictive maintenance and repair
Full text search on all plant data
Decision making aid services
Miras condition monitoring solution also represents an important step toward the digital factory – where all the players including machines, products and people along the value chain will be networked.
The interaction of Miras condition monitoring solution and our Leggoapp platform opens up completely new possibilities. The powerful bigdata platform is designed for analysing large quantities of data and enables machine all over the plant to be monitored for service purposes in order to reduce their downtimes – a big step toward digitization and also toward Industry.
Heart of Miras condition monitoring solution is its predictive maintenance technology. In order to implement this technology, our condition monitoring solution records and analyses mechanical variables (sensors data) from machines, integrates them into the automation world, and provides decision-making aids to maintenance staff, operators, and management. With this approach, control centres are able to closely monitor up-to-date status information. In the event of an anomaly, it is possible to quickly make the right decisions, such as estimating how much longer secure operation is possible based on the measured variables over time using machine learning algorithms. Also anomalies in a plant can be compared directly to the condition of surrounding components for example to determine whether an increase in temperature is an indication of a bearing overheating.
Smart Data predicts the future
Scheduled and reactive maintenance no longer provides the required results. With the continuous collection and intelligent analysis of operating data, digitalization has already opened up entirely new possibilities. This insight makes it possible to predict the best possible time for maintaining machine and plant components. There are many more possibilities – and they can play a key role in helping machine and plant operators achieve significantly higher productivity.
Predictive maintenance software solutions from Miras technology access multiple data sources in real time to predict asset failure or quality issues so your organization can avoid costly downtime and reduce maintenance costs. Driven by predictive analytics (machine learning based algorithms), these solutions detect even minor anomalies and failure patterns to determine the assets and operational processes that are at the greatest risk of problems or failure. This early identification of potential concerns helps you deploy limited resources more cost effectively, maximize equipment uptime and enhance quality and supply chain processes, ultimately improving customer satisfaction.
Miras predictive maintenance solution can help your organization:
Predict where, when are likely to occur
Minimize product quality and reliability issues to meet customer delivery schedules by better understanding asset performance and product quality
Optimize spare-parts inventory to reduce inventory costs associated with stockouts and overstocks
Enhance operations planning to reduce operations costs
Inform upcoming issues to planning and budgeting teams prior to costly event failures occurring
The digital transformation's importance for businesses is demonstrated by a global survey that the IBM Institute for Business Value and the University of Oxford's Saïd Business School conducted in 2012. The survey covered more than 1,000 experts from a variety of sectors. Almost two thirds of the people surveyed said that the use of data and analytical processes provides their companies with a competitive edge.
However, it's not just a question of managing the sheer mass of data, but also of controlling the speed and variety of the data. That's a huge challenge, because the digital universe is expected to consist of 40 zettabytes of data in 2020, according to a study conducted by the market research and consulting firm International Data Corporation (IDC). A zettabyte has 21 zeros. The increase would mean that the data volume would grow 50-fold within ten years. In its study, IDC also estimates that only three percent of the world's data has been tagged to date so that it can be found on the Web under the appropriate subject headings. The amount of data that is actually being analyzed is even lower. IDC calls this situation the big data gap.
Here in Miras, we are constantly developing effective tools to deal with this gap of bigdata. We are focusing on the domain expertise using sharp and talented data developers who are able to make sense of data.
To conduct an ongoing analysis of your business and improve your operations, your business processes must be evaluated in light of big data and IoT.
Miras sees the IoT technology as a platform for generating smart data all over its solutions. It is spanning from media monitoring to predictive maintenance & condition monitoring, from simple recoding voice recorder machine to plant Machin to Machin communication for sake of predictive maintenance.
Maintenance costs go beyond the actual price a business must pay to repair or replace a piece of machinery. Manufacturing and engineering businesses must also cope with the costs of operational delays and similar expenses that stem from an unexpected breakdown. IoT, however, stands to eliminate some of those costly (and often destructive) expenses through improvements to predictive maintenance.
With regard to predictive maintenance, machine to machine communication and the IoT provide ample benefits to industrial businesses for predicting problems with machinery. However, connected devices can also store copious amounts of data and provide "snapshots" of every machine's life cycle since the business began using the Miras technology.
IoT devices play decisive role in Marketing and PR world. These devices now can be deploy in focus group members at home and monitor the behaviour of focus group members using speech processing algorithms without violating their privacy. These technologies also extract more and more information from collecting data by cycle of learning.
By taking advantage of machine learning smart devices can learn from experience. As businesses use them, they collect and apply data that applies specifically to the equipment in question or focus group data in marketing campaigns. Based on previous events, for instance, the device might learn that when a certain set of variables appears, breakdowns become imminent or people actually watching which TV channels and for how long.