Implementing Predictive Maintenance systems – significant time and cost benefits in all production areas
Unforeseen machine failures result in performance interruptions, delivery bottlenecks and lack of quality. Predictive Maintenance is the maintenance strategy of the future, which renders it a vital part of Industry 4.0. Condition Monitoring is supplemented by a statistical analysis of the data recorded and predictions of future disruptive events.
The challenge for the quality of the prediction is to ensure the data is correct and reliable. Our background is in technology, and we occupy a special place in the market for Big Data solutions and data analytics – we know how this data needs to be gathered from your machine pool, your production system. Our services include implementing PM in your application scenario throughout the process, from data capture, through analysis and modelling to implementing a solution in your IT landscape.
The ESE Analytics Tool Chain links the world of data analytics to the user’s requirements:
- Professional support.
We develop your own application for Condition Monitoring and Predictive Maintenance, test and optimise the data quality, and implement the application in your enterprise IT.
Selection of memory and analysis platform
The Data Lake Principle
A Data Lake is a very large data memory that contains data from various sources in raw format. The data format can be both structured and unstructured, which makes it significantly different from a data warehouse, where the data must first be brought into a certain format by an ETL process (Extract, Transform, Load). Saving the data in raw format offers the advantage that the data can be used for many different applications by various analysis tools - also for analysis in the future. A Data Lake avoids distributed and redundant data storage. The use of a Data Lake can be flexibly provided by common frameworks such as Apache Hadoop. Hadoop overcomes the limitations of a typical data warehouse and reduces its operating costs. Hadoop is a distributed system of computers that can be combined in a cluster to scale freely.
Our consultants will be happy to advise you on your questions regarding the architecture of data storage systems and implementation of these for you.
MindSphere offers a wide range of protocol options for device and enterprise applications, industry applications, extensive analytics and an innovative development environment that leverages both Siemens' open platform-as-a-service (PaaS) capabilities and access to AWS cloud services.
Through these capabilities, MindSphere connects real things to the digital world and delivers powerful industry applications and digital services that enhance business success.
MindSphere enables the development and deployment of new industry applications in a diverse partner ecosystem through open PaaS capabilities. Benefit from the experience and insights of our partners. No development on your part is required to advance your IoT strategy.
Siemens offers business-oriented solutions that drive closed-loop innovation through digital twins for products, production and performance.
Splunk provides a scalable, versatile platform for machine data generated by all the devices, control systems, sensors, SCADA systems, networks, applications and end users networked by today's networks. Use Splunk for applications such as industrial production and maintenance, security, protection and compliance, and device analysis.
- Automate the collection and indexing of machine data and the triggering of notifications that are critical to your business processes
- Gain reliable insight from all your data, whether structured or unstructured
- Provide artificial intelligence through machine learning to make better informed decisions faster
Functional safety through expert opinion from a single source
Industrial control and automation technology has to meet the highest requirements in terms of safety and availability. Every change to an existing system has effects whose absence of retroactive effects on functional safety must be considered.
The Assessment Service Center (ASC) of ESE GmbH supports manufacturers of industrial control and automation technology in fulfilling these criteria. We offer our customers comprehensive services.
Find out more about the service range of the Assessment Service Center here.
Analysis and visualization
The maintenance of technical equipment is currently carried out on a cyclical basis. The actual condition of a machine is not taken into account. This means that maintenance is carried out that is not necessary, for example, because the machine has not been used. In the worst case, the machine will fail because maintenance was not carried out on time. Both scenarios cost operators a lot of money.
Predictive maintenance should detect defects at an early stage and set the maintenance process in motion. Technologies such as machine learning are used here.
As a branch of artificial intelligence, machine learning is able to find correlations in complex data through pattern recognition. To do this, the algorithms need a data basis in order to be trained on it. The learned result is then applied iteratively to new data in order to provide correlations and thus also results.