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Condition Monitoring

Siemens Condition Monitoring uses intelligent data analysis to enhance production machine efficiency, minimize downtime and optimize manufacturing processes.

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Connectivity

Machine-to-business intelligent system data connection is a fundamental aspect of modern manufacturing. Our condition monitoring system covers three crucial layers: machine connectivity, data storage and data presentation. In the first layer, we can collect data from machines and other sources such as SAP, MES and other ERP systems. Machine connectivity is the initial step for digital transformation in the industry.

Our great advantage lies in our in-depth knowledge of industrial control systems. We can provide comprehensive connection solutions for your machine portfolio, regardless of type or age. You get valid data you can rely on. We focus on data mining concerning production, maintenance, technology and business management.

We offer the implementation of these communication data gateways to maintain reliable and secure data gathering from production machines. We usually use one or more gateways to deepen the project scale and best practices. Several machines can communicate with one gateway.

An operator looks at the interface of Brownfield Analytics connectivity service on a large screen.
Computer screen showing full transparency into machine operating states

Data presentation

The main task of the condition monitoring system is a trusted analysis of the collected data in the form of an interactive web environment or reporting service for presentation purposes.

The system offers comprehensive set of dashboards and reports for production, maintenance and technology conditions. It brings transparency to your manufacturing processes and maintenance management.

For maintenance purposes, an advanced application is available. The predictive maintenance app congregates alarm monitoring, axis motor work hours, current load, speed and temperature monitoring of the machine's axis. Modern machine learning and statistics approaches are applied to determine the wear rate of specific components.

You can test data presentation capabilities in the free live demo.

Data analysis and process optimization

Analytics services leverage all data across the industry. The application of statistics and machine learning provides valuable information from big data. Our Analytical Services transform complex data into information with context to support your operational efficiency and innovation.

Predictive maintenance, quality control, resource management and reporting service applications are available in our solution set. Each presented tool has been developed in synergy with best industry practices and customizability to customer requirements. The Analytical Services applications are also available in the live demo app.

A detailed analysis and comparison of the MTBF indicator using Siemens Condition Monitoring.
A list of monitored signals and detected anomalies using Siemens Condition Monitoring.

PMApp – Predictive Maintenance App

The Siemens Predictive Maintenance System (PMApp) is a solution for monitoring and detailed analysis of collected condition data to detect the initial occurrence of failure or degradation of the monitored equipment.

The system fits into the condition-based maintenance strategy. Its main purpose is to optimize maintenance processes and reduce maintenance costs while improving operational efficiency and minimizing downtime of production and processing equipment. This modern approach to maintenance management is a significant step towards achieving excellence in this field. The Siemens Predictive Maintenance solution can be applied across a variant of industrial applications, including manufacturing, chemical, metallurgical or food processing plants that use data-generating machinery.

Signal Processing module

The Signal Processing module processes and monitors the collected time series from industrial machinery, or process sensors, which deviations from standard behavior need to be under surveillance. Signal Processing automatically and reliably detects any anomalies arising during the operation of the monitored component and initiates the maintenance department to schedule a service intervention.

A detailed view of an analysis of a monitored signal using Siemens Condition Monitoring.
Advanced analysis of detected critical alarms using Siemens Condition Monitoring.

Smart Alarm Analysis module

The Smart Alarm Analysis module is specified to perform an automated analysis of recorded text information, which may contain relevant information about the technical state of the monitored equipment. Text information can come from various sources like machinery control systems, diagnostic devices, or company information portals.

An alarm module is the best solution for situations when the number of generated alarms and messages exceeds the limitation of the maintenance department that can't process them independently. Only a fraction of all collected texts contain valuable information about an emerging failure. The module identifies critical text information and prevents a general failure by on-time maintenance warnings.

Work Hours Analysis module

The Work Hours Analysis module provides advanced analysis of machine or component uptime. The analysis outputs are the probability of machine failure or estimated mean time to failure. 

It allows you to optimize maintenance and production planning processes according to acquired knowledge. You can also schedule preventive repairs according to real severity and real workload. Lastly, you can distribute the production orders across the workshop according to machine reliability indicators (MTBF, MTTR) and estimated availability.

Trend of MTBF, MTTR and Availability with calculated probability of further operation and estimated time to next failure.
A prepared web production report using RSApp from Siemens.

RSApp – Report Service App

The Report Service Application (RSApp) combines, at regular intervals, large amounts of process data from various company sources (machine data, SAP, MES, etc.) to help management and company personnel with analysis of production, maintenance, technology, planning or controlling. With the RSApp system, employees without advanced knowledge of data analytics can save time, focus on problem-solving and optimize processes.

The output of the Report Service can be a web-based interactive visualization, an offline PDF or PowerPoint report or prepared data in the company's database for further processing in BI software (e.g. Tableau or PowerBI).

Features of the reports are key performance indicators (KPI) with their trends and targets, data visualization in context (bar, pie, trend graphs, etc.), reports on utilization and downtime in production, statistics of production interruptions, evaluation of machine or worker performance and more.

RSApp also includes the ad-hoc analysis module, an extraordinary service within Siemens Analytical Services that allows you to order focused analysis of a wide range of situations or processes. The results are available in the Report Service Application after drafting. You can adapt ad-hoc analysis to support the decision-making process of employees and management.

We provide services such as pattern detection, known and unknown relationships, looking for waste and causes in downtime, non-standard conditions and behaviors detection, optimization of company and production processes and other valuable information extraction.

QCApp – Quality Control App

The Quality Control Application (QCApp) focuses on the quality of the manufacturing processes. It provides a comprehensive system for production management and quality control departments to take control of manufacturing processes. The main goals of optimization are to minimize defects and maximize your product's reliability.

The QCApp consists of three tools. The Anomaly Detection tool can classify a non-conforming in early production phases by process signals generated during production or in photographs captured after the machining or assembly process.

The Statistical Process Control (SPC) tool focuses on online monitoring of the production process, detecting any non-standard production phenomena. SPC is a simple but effective technique to increase productivity, prevent defects and avoid unnecessary modifications to the production process.

Last but not least is the user-interactive Design of Experiments (DOE) tool, which allows experimentation with different inputs and settings of the production process. It leads to significant quality effects and production performance identification.

The dashboard for online monitoring of production process quality in QCApp from Siemens.
The SMApp dashboard for monitoring spare parts in the maintenance department's inventory.

SMApp – Supply Management App

The Supply Management Application (SMApp) enables the analysis and optimization of production material inventories, maintenance spare parts warehouse and transportation routes within internal logistics.

Based on the analysis of spare parts or material history consumption and level of risk or coverage reliability, the application predicts optimal stock. Internal logistics can profit from SMApp by optimizing settings of transport routes for inventories to ideally utilize the capacity of logistics staff. It saves transport time and energy.

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