Realizing bottom line growth for manufacturing

Predictive Maintenance is Difficult!...

Do you feel that way?


  • Improved Production Efficiency
  • Historical Logs
  • Quick ROI
  • Open Source

Product Features

  • Easy Installation
  • Flexibility
  • Compare FFT Analysis
  • Multiligual Support
  • Root Cause Analysis


Manufacturers are required to assess their traditional methods and make changes to streamline their operations. By applying advanced analytics to manufacturers’ data can produce insights to optimize the productivity of individual assets as well as the total manufacturing operation.

Reduce maintainence cost upto 15%*


Predictive analytics solution can be implemented in order to monitor health of equipment throughout the production process like Press Shop, Body Shop, Paint Shop, Assembly Shop, Power Train, Factory Air Supply fans and many more equipment’s.

Reduce maintainence cost upto 15%*
IMPROVE QUALITY AND Optimize maintainence cost upto 15%*

Steel Industry

Condition monitoring is a key tool for addressing quality issues particularly for critical finish rolling mill application. Early detection of quality defects on flat product and identifying the source allow the mill to take corrective actions and minimize the impact to production.

Reduce maintainence cost upto 10%*

Pharmaceutical Industry

Raw materials such as powder, bulk drugs are dried at extreme heat which can result in lubrication issues and unexpected failures. Our aim is to improve the critical machine availability, to prevent machine breakdown and support operational. profitability

Reduce maintainence cost upto 10%*
IMPROVE QUALITY AND Optimize maintainence cost upto 10%*

Food and Beverage

In Food and Beverages industry, maintaining high level of quality is paramount. Unexpected breakdowns can lead to loss of products, contamination and cause damage to secondary machines.


Case I : Detect an abnormal sign and perform maintenance before failure

The changes in the equipment condition can be observed by monitoring the vibration from equipment.

However, the difference between the normal and abnormal waveforms is unclear in the time domain, making it difficult to detect an abnormal sign.

By using advanced algorithms

Detect an abnormal sign by converting the time-domain waveform to the frequency-domain waveform!

Presume the faulty area according to the simple diagnosis and accurate diagnosis!

Case II : Detect "unusual" conditions by MT method*1 diagnosis!

Generate unit space by collecting more than the required amount of vibration data under normal conditions before diagnosis.

Quantify the amount of deviation from the unit space with a single index called Mahalanobis distance and determine if it is normal or abnormal.

Combined diagnosis is possible by combining data other than vibration such as temperature and current.

What’s included?

The system configuration example with the rotary machine vibration diagnosis package is shown below.

Schedule free consultation with Mitsubishi Electric Digital Transformation expert

Free demo & consultation for end to end solution of your custom challenges for production/manufacturing equipment’s. Schedule at your convenience. today


For Installation, reporting details, display dashboards, technical specification and more details

*Figures quoted are considering standard operational procedures/processes within the industry. To know benefits related to your plant conditions, and for free consultation, kindly contact our Digital Transformation Expert.
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