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Yokogawa releases new software “Gas chromatograph AI maintenance support”


Yokogawa releases new software “Gas chromatograph AI maintenance support”

Yokogawa Electric Corporation announced the release of a gas chromatograph AI maintenance support tool (hereafter “GCAI tool”) that increases maintenance efficiency for the GC8000 process gas chromatograph, a product in the OpreX Analyzer family.

Source: ARCweb

This software is capable of detecting slight changes in measurement data that are an indication of an impending instrument failure before this can begin to have an impact on measurements. By allowing maintenance to be performed in a timely manner, downtime is reduced.

New features of the GCAI tool software are as follows:

Data-based detection of measurement soundness

Slight changes shown in the chromatogram data appear even before the wearing out of a component or some other issue begins to impact measurements. Though visible, they can have many different patterns, and their position on a time axis may differ depending on the instrument, meaning that only highly-experienced operators can distinguish between them.

The GCAI tool uses a machine learning model that configures individual GC8000 units. This model is used to detect anomalies by identifying variances from normal measurements, as shown in a chromatogram. Each time a variance from a normal state is identified, the model analyzes the degree of variance. It can pick up even slight changes, and by determining the soundness each time a measurement is performed, it provides information that can be used to plan and implement maintenance before any deterioration in performance can have an impact on measurement values.

Prediction of measurement soundness 90 days in advance

With chromatographs, gases are separated and measured on a time axis, and poor separation of the gas components impacts measurement quality. Using this degree of separation as an index, the GCAI tool can predict when maintenance will be required. Based on changes in the degree of separation, it can predict the degree of separation 90 days in advance and provide notification when a predefined degree of separation is about to be reached. Knowing in advance when maintenance will be required makes it possible to identify which replacement components need to be ordered and to create an appropriate and effective maintenance plan for the instrument, thus helping to reduce downtime.

No need for consulting or complex device configuration

For the GCAI tool to automatically detect and record changes in the measurement data, users need only to set a reference date, area to be monitored, and notification threshold. This AI solution for predicting the soundness of measurements is easy to use and requires no special consulting or complex device configuration. The import and pre-processing of data used for machine learning is also automated. And as this software is offered on an annual subscription basis, users do not need to make a large initial investment.

Major target markets include oil & gas, petrochemicals, steel, and electric power.

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