Industrial Control under Non-Ideal Measurements: Data-Based Signal Processing as an Alternative to Controller Retuning

Abstract

Industrial environments are characterised by the non-lineal and highly complex processes they perform. Different control strategies are considered to assure that these processes are correctly performed. Nevertheless, these strategies are sensible to noise-corrupted and delayed measurements. For that reason, denoising techniques and delay correction methodologies should be considered but, most of these techniques require a complex design and optimisation process as a function of the scenario where they are applied. To alleviate this, a complete data-based approach devoted to denoising and correcting the delay of measurements is proposed here with a two-fold objective: simplify the solution design process and achieve its decoupling from the considered control strategy as well as from the scenario. Here it corresponds to a Wastewater Treatment Plant (WWTP). However, the proposed solution can be adopted at any industrial environment since neither an optimization nor a design focused on the scenario is required, only pairs of input and output data. Results show that a minimum Root Mean Squared Error (RMSE) improvement of a 63.87% is achieved when the new proposed data-based denoising approach is considered. In addition, the whole system performance show that similar and even better results are obtained when compared to scenario-optimised methodologies.

A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends

“…Data preprocessing is required in order to treat the data and obtain clean data that can be useful for feature extraction. Even if in some scenarios, features are extracted directly from the raw signals, the high complexity of the industrial systems and the nonlinear processes involved ask for the application of signal processing strategies [90]. These strategies may include the removal of the effects of the operating conditions from the acquired signals, the removal of noise in noisy signals, the identification and removal of outliers that may lead to non-significant features, and the transformation of the signals to other domains from which key features can be extracted, among others.…”