Detecting Industrial Fouling by Monotonicity During Ultrasonic Cleaning

Rajani, Chang; Klami, Arto; Salmi, Ari; Rauhala, Timo; Haggstrom, Edward; Myllymäki, Petri

doi:10.1109/mlsp.2018.8517080

Cited by 7 publications

(8 citation statements)

References 10 publications

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“…Concerning the use of NN for regression purposes in cleaning processes, the available research is considerably limited, such as in applications to heat exchanger fouling assessment [36] during ultrasonic cleaning using Convolutional Neural Networks (CNN).…”

Section: Signal and Image Processingmentioning

confidence: 99%

Intelligent Industrial Cleaning: A Multi-Sensor Approach Utilising Machine Learning-Based Regression

Simeone

Woolley

Escrig

et al. 2020

Sensors

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Effectively cleaning equipment is essential for the safe production of food but requires a significant amount of time and resources such as water, energy, and chemicals. To optimize the cleaning of food production equipment, there is the need for innovative technologies to monitor the removal of fouling from equipment surfaces. In this work, optical and ultrasonic sensors are used to monitor the fouling removal of food materials with different physicochemical properties from a benchtop rig. Tailored signal and image processing procedures are developed to monitor the cleaning process, and a neural network regression model is developed to predict the amount of fouling remaining on the surface. The results show that the three dissimilar food fouling materials investigated were removed from the test section via different cleaning mechanisms, and the neural network models were able to predict the area and volume of fouling present during cleaning with accuracies as high as 98% and 97%, respectively. This work demonstrates that sensors and machine learning methods can be effectively combined to monitor cleaning processes.

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Section: Signal and Image Processingmentioning

confidence: 99%

Intelligent Industrial Cleaning: A Multi-Sensor Approach Utilising Machine Learning-Based Regression

Simeone

Woolley

Escrig

et al. 2020

Sensors

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“…Most of the works concerning RUL estimation in bearings focus on creating and selecting features based on a monotonicity criterion [6,8,9,15,16,[20][21][22] or post-processing the predictions to fulfill monotonicity [23]. Similar to this approach, structural learning has been used to leverage temporal relations in time series data to induce consistency between continuous predictions [24] and impose monotonicity [25].…”

Section: Related Workmentioning

confidence: 99%

A Semi-Supervised Approach with Monotonic Constraints for Improved Remaining Useful Life Estimation

Avendano

Vandermoortele

Soete

et al. 2022

Sensors

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Remaining useful life is of great value in the industry and is a key component of Prognostics and Health Management (PHM) in the context of the Predictive Maintenance (PdM) strategy. Accurate estimation of the remaining useful life (RUL) is helpful for optimizing maintenance schedules, obtaining insights into the component degradation, and avoiding unexpected breakdowns. This paper presents a methodology for creating health index models with monotonicity in a semi-supervised approach. The health indexes are then used for enhancing remaining useful life estimation models. The methodology is evaluated on two bearing datasets. Results demonstrate the advantage of using the monotonic health index for obtaining insights into the bearing degradation and for remaining useful life estimation.

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“…More flexible is the formation of assessment based on machine learning, which was used in [5] and [6]. In these works, the condition of the treatment facility (in both cases, part of the pipe) was assessed by applying a neural network to the received ultrasound responses.…”

Section: Introductionmentioning

confidence: 99%

Spatial ultrasonic cleaning process control based on its current state evaluation

Morkun

Kravchenko

2021

E3S Web Conf.

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Ultrasonic cleaning is one of the most promising types of cleaning in terms of environmental friendliness, cost and efficiency. The condition of the cleaning body must be taken into account for optimal control of the ultrasonic cleaning process. This allows you to irradiate only those areas that really need it. The modelling of the process of ultrasonic cleaning of bodies of different configurations and the analysis of the parameters of ultrasonic responses at different stages of cleaning were performed. This allowed us to identify the parameters by which the assessment of the process should be formed. The main parameter was the change in the time of receipt of the threshold value of the signal, and the auxiliary - the change of the nonlinearity coefficient of the second order. The change in the time of receipt of the threshold value of the signal is an indicator of dirt peeling, and the change in the nonlinearity coefficient demonstrates the approach to the final result of cleaning. These parameters became clear input data for the 3-D fuzzy interval controller. The functions of affiliation were defined and the base of rules was formed. Modelling of the ultrasonic cleaning process using the established method of estimating the course of the process and the use of 3-D fuzzy interval controller showed about 35%energy savings.

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Detecting Industrial Fouling by Monotonicity During Ultrasonic Cleaning

Cited by 7 publications

References 10 publications

Intelligent Industrial Cleaning: A Multi-Sensor Approach Utilising Machine Learning-Based Regression

Intelligent Industrial Cleaning: A Multi-Sensor Approach Utilising Machine Learning-Based Regression

A Semi-Supervised Approach with Monotonic Constraints for Improved Remaining Useful Life Estimation

Spatial ultrasonic cleaning process control based on its current state evaluation

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