Tracking Operation Conditions and Trend Health of Rotating Vibrations in Industrial Machinery

The easy-to-setup Vibration Analyzer from MachineSense™ continuously monitors the operating condition as well as trend health of rotating vibrations in industrial machinery.

Constant tracking allows early detection of symptoms that emerge under inconsistent operating conditions

Monitors trend health and ambient operating conditions of rotating machinery vibrations around the clock

Local EDGE analytics allow constant high-speed data tracking, increased data integrity, and minimized data transfer

Cloud-based dashboard gauges summarize modern analytics into easily understandable outcomes that need no advanced training or expertise

The easily understandable icons guide users to view vibration health trends showing up to six months of operating history. Threshold values can be personalized, thus enabling users to determine unhealthy operating conditions, desired maintenance conditions, or deteriorated vibration health.

Patented time-domain data sampling techniques allow constant trend tracking to identify anomalies and replicable events that take place under varying process or operating conditions, thus enabling early detection of developing vibration health problems, as well as the operating trends that might be causing them.

Conventional Frequency-Domain data and analytics (coming soon) add optimized diagnosis abilities for bearing and gearbox faults at the beginning, with other device libraries being added every month.

Visualization of major operating and analytic findings are comfortably offered round the clock through a web browser or mobile app in easily understandable dashboard gauges, as well as through text or email alerts. There is no need for modern training or expertise.

Understanding the Value of Predictive Maintenance

Optimization of assets usage and ensuring increased productivity are some of the most crucial goals in the manufacturing sector. Reliable assets decrease downtime, enhance production quality, and enable a product to hit the market quickly.

Two Types of Downtime

Scheduled

Regular cleaning and maintenance, installing updates, etc.

Unscheduled

Repairs to equipment that has stopped performing its assigned function or is not performing its function adequately.

Measures and Predicts

Abnormal vibration

Machine utilization

Bearing health

Ambient operating conditions

Sensor installation status

Belt-drive misalignment

Preventing Machine Downtime

40% of downtime is caused due to software failure or malfunction

46% of downtime is caused as a result of machinery malfunction or failure

70% or more companies do not have much awareness of when the equipment is due for an upgrade, maintenance, or replacement.

Poorly maintained equipment leads to profit loss and lost production time. Unplanned repairs are expensive. Some studies show that repairs constitute 15% of the total expenses.

Predictive Maintenance

By installing predictive maintenance equipment, users can easily find out the condition of their processes. The correct system offers actionable intelligence, which gives an alert of approaching failure if reported problems are not mitigated.

Does it Really Work?

The perfect predictive maintenance system will enable users to schedule maintenance before equipment failure, which enables avoiding unscheduled downtime, decreasing repair charges and equipment failures, as well as slowing down asset deterioration.

Does it Really Save Money?

A predictive maintenance program is not just considerably less expensive compared to a reactive program—few studies claim up to 40% cheaper maintenance—but for each $1 spent to enhance equipment, there is a $5–10 return to the bottom line.

But, is it Really Easy to Install?

The wearable solutions available from MachineSense™ scale up existing equipment non-invasively. The solutions make it very easy and affordable to upgrade users’ present equipment with simple-to-integrate vibration and electrical sensors that track crucial systems and assets.

Start the Device up

The most effective method is to utilize a predictive maintenance solution that can be easily installed in users’ present equipment and has a robust analytics software package that gathers data from users’ monitored equipment in real-time, compares the data with baselines determined already, evaluates the condition of the equipment, and makes use of analytics to effectively control maintenance activities.

With the help of MachineSense™, users will come to know precisely what is going on with their machines, thus enabling them to avoid unplanned downtime and enhance profits and productivity considerably.