CMMS Condition Monitoring Reliability Software

Integrate A CMS To Monitor Asset Health

EP Editorial Staff | April 8, 2021

Sensors connecting assets to an integrated condition-monitoring system greatly bolster reliability and productivity by detecting abnormal patterns and predicting downtime events well ahead of actual failure.

Advanced condition monitoring solutions reliably track asset performance and prevent costly downtime.

By Luis Narvaez, Siemens Industry

Staying competitive in today’s environment is challenging. It’s made more difficult as customers seek customized products at the prices of mass-produced goods. Furthermore, growing global ecological awareness and rising energy prices are motivating many companies to work toward carbon neutrality, for reasons of cost and reputation. These, and other factors, require manufacturers to assess and potentially modify their production processes.

To face these challenges, manufacturers have access to modern tools that propel them to success in the digital age, but they must be vigilant to ensure maximum efficiency and subsequent profitability. Unplanned downtime is a key inhibiting factor to achieving these objectives. Although it’s impossible to completely eliminate downtime, an integrated condition-monitoring system (CMS) can provide a reliable and cost-effective means of keeping assets operating at optimum efficiency.

An integrated CMS, such as a Siemens SIPLUS CMS 1200, allows modular expansion and simple integration into automation systems.

New Maintenance Standard

A modern integrated CMS greatly bolsters system reliability and productivity without breaking the bank by analyzing real-time condition and performance data collected by sensors. These systems can detect abnormal patterns and predict downtime events well ahead of actual failure. This data is also used to pinpoint root causes. Unlike preventive-maintenance programs, maintenance is only scheduled when necessary to mitigate equipment issues.

Many issues are caused by excessive machine vibration, resulting in premature failures. While maintenance personnel and operators can sense vibration to a point by touch and hearing, vibration sensors are much more effective. By installing integrated electronic piezo-electric (IEPE) vibration sensors in machines and connecting them to an integrated CMS, users gain early detection insights in a digitized format. The sensors’ monitoring hub can be configured to generate operator notifications or modify production, for example by reducing motor speed.

Modern integrated CMS modules plug into PLC backplanes, with the option for accessibility over the production network or a separate subnet, including through secure cloud VPN connectivity. These modules communicate data directly with a host PLC, enabling control based on equipment data from the condition-monitoring module and process data from other PLC inputs.

Depending on network configuration, module data can be accessed through a PLC’s integrated automation suite or through onboard webservers using a browser from any networked device. This means no special software is required to view equipment health data. Because these modules perform built-in data logging, users can review machine performance over time through the web-based dashboards.

These CMSs come primed for connectivity, including communication using PROFINET, MQTT, OPC-UA, and Modbus TCP protocols. Each condition-monitoring module has four ports for connecting IEPE vibration sensors and these modules support characteristic-based and frequency-selective vibration analysis, which can be streamed or exported for further analysis using outside tools or cloud services.

The Siemens SM 1281 condition-monitoring module has four ports for connected vibration sensors. Data is accessible through a web interface using a browser from any networked device.

Characteristic-based analysis is, in essence, machine health monitoring using root mean square analysis of mechanical velocity and acceleration data. Such analysis is easily configured and monitored for bearings and other components, and alerts can be generated when movements exceed manufacturer recommendations. While this is a great starting point for effective predictive maintenance, characteristic-based analysis alone does not reveal what type of vibration damage is occurring.

To discover the type of damage, frequency-selective analysis can be applied by breaking up the net vibration frequency recognized in characteristic-based analysis into its contained collection of vibration signals. These signals are superimposed over their respective frequency spectrum using a Fast Fourier Transformation (FFT) algorithm, with the CMS performing this mathematical operation internally.

The result of the FFT calculations reveals the following types of bearing damage:

• unbalance eccentricity
• misalignment soft foot
rotating looseness
rotor rubbing
vane faults
electrical field faults
gear meshing faults
roller bearing noise.

The FFT analysis can also be configured and visualized through integrated webservers.


For a manufacturer using a CMS on a milling machine, early warning of bearing failure empowered them to minimize downtime. As later snapshots revealed, the machine was experiencing increasing vibrations over time, but problems were undetectable by human sensory. When vibration reached the preconfigured maximum, the CMS generated an alert to notify staff.

Lead time on a replacement bearing was several months, but the early warning provided time to continue production while waiting. Once the replacement part arrived and was installed, they found considerable damage on the old bearing. The bearing would have probably failed completely if left unmitigated, so early detection may have spared the manufacturer from months of unplanned downtime.

Future of Predictive Maintenance

Early detection of mechanical and electrical damage is one of the most effective means for reducing unplanned downtime and an integrated CMS can provide transparency into machine health to make this possible. Furthermore, these notifications assist operations, management, maintenance, resource planning, and engineering staff to perform their jobs more effectively by forecasting machine failure.

The future of condition monitoring will expand upon predictive maintenance by performing prescriptive maintenance to provide additional insight into the cause of problems, along with suggested mitigation remedies. Edge- and cloud-based solutions will likely also play larger roles as machine-learning algorithms become more refined. EP

Luis Narvaez is the S7-1200, S7-1200F, and SIPLUS CMS product manager for Siemens Industry, Washington, DC ( Narvaez has more than ten years of industry experience in various roles within the construction, entertainment/theme park, manufacturing, and process industries.



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