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Soft-sensing for compressor test time reduction with time-delay neural networks.

Bernardo B Schwedersky¹, Rodolfo C C Flesch², João P Z Machado²

¹Centro de Desenvolvimento Tecnológico, Universidade Federal de Pelotas, Rua Gomes Carneiro 01, Pelotas, 96010-610, RS, Brazil.

ISA Transactions

|February 15, 2026

Summary

This summary is machine-generated.

This study introduces a soft-sensor method using a time-delay neural network (TDNN) to speed up compressor performance tests. The approach significantly cuts evaluation times, saving nearly 50% in industrial settings.

Keywords:

Compressor performance Condition monitoring Industrial application Soft sensors Time-delay neural networks

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Area of Science:

Mechanical Engineering
Control Systems Engineering

Background:

Traditional compressor performance evaluation tests are lengthy, often exceeding two hours to reach steady-state conditions.
Optimizing testing efficiency is crucial for manufacturers to reduce operational costs and improve throughput.

Purpose of the Study:

To develop and validate a soft-sensor-based method for significantly shortening compressor performance evaluation test durations.
To monitor key performance parameters and predict their final values for early steady-state detection.

Main Methods:

Development of a soft sensor utilizing a time-delay neural network (TDNN).
Training the TDNN model on a dataset of 392 historical compressor evaluations.
Implementing the soft-sensing approach for real-time monitoring and prediction of steady-state conditions.

Main Results:

The proposed method achieved an approximate 50% reduction in test duration during initial development.
Over five years of industrial application, 9184 performance evaluations demonstrated a 55% improvement in total test time.
More than 95% of industrial tests exhibited prediction errors below 2%.

Conclusions:

The soft-sensor-based method effectively accelerates compressor performance evaluations, leading to substantial time savings.
The TDNN approach enhances operational efficiency and provides reliable predictions for key performance parameters.
Consistent industrial application validates the method's effectiveness and robustness in real-world manufacturing environments.