SAY GOODBYE TO SCRAPPED PARTS.

Supercharge your predictive analytics with high-frequency machine data. Diagnose, predict, and avoid failures with unprecedented visibility into the condition of your machines with no sensors required.

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DIAGNOSE
Gain unprecedented fidelity with plug-and-play high frequency data collection.
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PREDICT
Leverage transformed data to immediately identify problems never visible before.
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PREVENT
Deliver solutions to prevent problems before they cause expensive downtime events.
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Most manufacturers are left blind as to when their machines will go down. The result is costly unplanned downtime, unexpected maintenance events, and a reduction in throughput. While there are hidden indicators of when machines are likely to fail, it’s impossible to find these insights by manually capturing machine performance and health data

The Current State of Things
The Problem TODAY’S SOLUTIONS DON’T CUT IT
Unscalable

Aftermarket sensor installations can be difficult to standardize, difficult to install, and are subject to degradation

UNRELIABLE

Where do you place the sensor, and what happens when it gets bumped out of place? Can your customer install and tune the sensor themselves?

INACCURATE

Imagine you’re trying to learn a new tune on the piano, but the sheet music only has one note out of every ten.

EXPENSIVE

Installation time and costs of sensors add up dramatically.

High Frequency Data

In 2020, our data science team launched a program focused on the application of high-frequency machine data for predictive maintenance with the goal of accelerating predictive analytics use cases for machine tools. Our team discovered a way to collect data at 1 kHz directly from the control of CNC machines without using sensors that can immediately be used as inputs to time-series or machine learning models to predict machine failures.

The Discovery
THE SOLUTION
STOP FAILURES BEFORE THEY HAPPEN

Supercharge your predictive analytics applications with high-frequency machine data to diagnose, predict, and avoid failures on your manufacturing equipment. No sensors required.

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IDENTIFY THE PROBLEM
Leverage high-frequency data directly from the machine control or from sensor data. Use this data as inputs to time-series or machine learning models.
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DEPLOY AT THE EDGE
Deploy and manage custom algorithms to MachineMetrics Edge devices that process and analyze at the source to detect potential failures.
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AUTOMATE SOLUTIONS
When an algorithm is triggered, deliver operator actions via alerts/notifications to factory workers or automate a feed-hold that stops machines prior to equipment failure.
USE CASES
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Use Case #1 Machine Diagnostics

Understanding why issues have occured on machines in the past has been a notoriously difficult task. While low-frequency data can accomplish condition-based monitoring, Trying to do condition-based monitoring on say, spindle load, requires higher fidelity.

Collecting high frequency data from the control delivers unprecedented visibility and fidelity into the health and condition of the machine, enabling diagnoses that were once impossible.
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Use Case #2 Predictive Maintenance

Use key indicators within your machine performance and condition data to trigger action before a downtime event occurs.

Transform from a calendar-based maintenance program to condition-based or predictive maintenance.
How do we know this is a game-changer?
A Case Study BC Machining
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Leveraging high-frequency data on spindle load to determine when the tools will fail, BC Machining can now detect a tool failure with 99% confidence up to 40 minutes before it fails.

We are actuating the machine to perform a Feed-Hold when this algorithm is triggered to stop the breakage from happening.

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