Predictive Maintenance Solutions

Predictive Maintenance Solutions

According to a recent report published by Interact Analysis, industries have a higher vested interest in preventing the failure of certain equipment as it could represent the complete loss of a batch being manufactured. This type of failure often results in a significant financial loss. (The Market for Predictive Maintenance in Motor Driven Systems)

Predictive maintenance is becoming essential to the smart factory. Is your equipment at risk? Do you have a solution in place to monitor your crucial machine components and minimize downtime needed for repairs?

Our partners at Banner Engineering have developed products and solutions to accurately track machine performance and anticipate failures before they occur.

Key Elements of a Predictive Maintenance Solution

Condition Monitoring

Condition monitoring plays a key role in predictive maintenance by allowing users to identify critical changes in machine performance. One important condition to monitor is vibration. Machine vibration is often caused by imbalanced, misaligned, loose, or worn parts.

As vibration increases, so can damage to the machine. By monitoring motors, pumps, compressors, fans, blowers, and gearboxes for increases in vibration, problems can be detected before they become severe and result in unplanned downtime.

Vibration sensors typically measure RMS velocity, which provides the most uniform measurement of vibration over a wide range of machine frequencies and is indicative of overall machine health.  Another key data point is temperature change (i.e. overheating).

Machine Learning

Machine learning takes condition monitoring data and automatically defines a machine’s baseline conditions and sets thresholds for acute and chronic conditions so that you know in advance–and with confidence–when your machine will require maintenance.

After mounting the vibration sensor onto your machine, most sensors require you to collect enough data to establish a baseline for the machine. Machine learning removes the chances of human error by automating the data analysis.

A condition monitoring solution with machine learning will recognize the machine’s unique baseline of vibration and temperature levels and automatically set warning and alert thresholds at the appropriate points. This makes the condition monitoring system more reliable and less dependent on error-prone manual calculations.

Indication & Data Logging

When a vibration or temperature threshold has been exceeded, a smart condition monitoring system provides both local indication, such as sending a signal to a tower light in a central location, and remote alerts like emails or text messages.  This ensures that warnings are addressed quickly.

In addition, a condition monitoring solution that allows you to log the collected data over time enables even more optimization.  With a wireless system, vibration and temperature data can be sent to a wireless controller or programmable logic controller (PLC) for in-depth, long-term analysis.

Product Portfolio

Wireless Solutions Kit

WirelessSolutionsKit2
Wireless Solutions Kits are fully integrated and easy-to-use solutions for monitoring assets and solving specific applications. They are designed to make it easy for users of any experience level to setup a wireless network, collect remote data, and create visualization tools, warnings, and alarms.

DXM Series

DXMWireless
DXM Series industrial wireless controllers are designed to facilitate Ethernet connectivity and Industrial Internet of Things (IIoT) applications.

QM30VT Series Sensor

QM30VT VibTempSensor
QM30VT Series sensors have a low-profile design and rigid metal construction that reduces resonant interference and increases surface contact, enabling exceptional levels of accuracy in measuring RMS velocity and temperature.

Request a Demo or a Quote

For more information or to request a quote, contact orders@shingle.com, or contact your local Shingle District Manager