Advanced Signal Processing Methods for Condition Monitoring

Jaroš, René; Byrtus, Radek; Dohnal, Jakub; Danys, Lukáš; Baroš, Jan; Koziorek, Jiří; Zmij, Petr; Martínek, Radek

doi:10.1007/s11831-022-09834-4

Cited by 26 publications

(18 citation statements)

References 113 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Power quality strongly affects the performance of powerusing equipment [15], [16]. Power quality determines the lifespan of electrical equipment [17].…”

Section: Theoretical Foundations and Hypothesis Developmentmentioning

confidence: 99%

Analysis of Factors Affecting Electric Power Quality: PLS-SEM and Deep Learning Neural Network Analysis

2023

View full text Add to dashboard Cite

The world today is increasingly dependent directly or indirectly on the power system. Ensuring the quality of power supplied to electrical equipment is essential. The national regulatory framework is for harmonic mitigation in the global power system. This paper discusses the relationship between Efficiency (E), Security (S), and Reliability (R) for Electric Power Quality (EPQ). We measure the harmonic mitigation regulations listed in the IEEE 519 standard. To evaluate the proposed E, S, and R constructs and their relationship to EPQ, a multi-planning approach the method of Partial Least Squares-Structural Equation Modeling (PLS-SEM) and Deep Learning Artificial Neural Network (ANN) analysis were performed. In it, deep Learning Artificial Neural Network (ANN) was performed to complement the PLS-SEM findings and higher prediction accuracy. The study shows that the aspects of efficiency (E), security (S), and reliability (R) have a significant relationship with Electric Power Quality (EPQ). Another result of the study indicates that science, technology, engineering and math (STEM) resource conditions have a significant and positive impact on EPQ.

show abstract

“…Power quality strongly affects the performance of powerusing equipment [15], [16]. Power quality determines the lifespan of electrical equipment [17].…”

Section: Theoretical Foundations and Hypothesis Developmentmentioning

confidence: 99%

Analysis of Factors Affecting Electric Power Quality: PLS-SEM and Deep Learning Neural Network Analysis

2023

View full text Add to dashboard Cite

show abstract

“…This integrated approach aims to reduce downtime [3], maintenance costs [4], and catastrophic failures [5,6]. In a variety of industries, condition monitoring [7] is a technique used to evaluate the performance and health of machines [8], tools [9], or systems in real time or on a regular basis. The aim of condition monitoring is to identify any changes from the normal operation, detect possible issues or faults early, and stop unexpected failures [10] or breakdowns [4].…”

Section: Introductionmentioning

confidence: 99%

“…Predictive maintenance helps prevent the occurrence of these breakdowns by predicting possible problems before they become catastrophic failures. These machines are equipped with sensors [25][26][27][28][29] that continuously collect data on parameters like temperature [9,20,25,30], pressure [7,20] and vibration [21]. These data are then evaluated by software, which uses cutting-edge methods such as machine learning [31], previous data, and contextual information to construct predictive models that identify expected issues or maintenance requirements based on usage patterns and environmental factors.…”

Section: Introductionmentioning

confidence: 99%

An In-Depth Study of Vibration Sensors for Condition Monitoring

Hassan,

Panduru,

Walsh

2024

Sensors

View full text Add to dashboard Cite

Heavy machinery allows for the efficient, precise, and safe management of large-scale operations that are beyond the abilities of humans. Heavy machinery breakdowns or failures lead to unexpected downtime, increasing maintenance costs, project delays, and leading to a negative impact on personnel safety. Predictive maintenance is a maintenance strategy that predicts possible breakdowns of equipment using data analysis, pattern recognition, and machine learning. In this paper, vibration-based condition monitoring studies are reviewed with a focus on the devices and methods used for data collection. For measuring vibrations, different accelerometers and their technologies were investigated and evaluated within data collection contexts. The studies collected information from a wide range of sources in the heavy machinery. Throughout our review, we came across some studies using simulations or existing datasets. We concluded in this review that due to the complexity of the situation, we need to use more advanced accelerometers that can measure vibration.

show abstract

“…Existing methods for bearing fault diagnosis can be divided into signal processing based and data-driven, considering the feature extraction methods [4,5]. Utilizing knowledge from the field of signal processing for bearing fault diagnosis has been proven to be an effective method, with the core idea being signal decomposition and filtering.…”

Section: Introductionmentioning

confidence: 99%

AdaClass filter and its application in bearing fault diagnosis

Zhang,

Li,

Zhang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

The vibration signals recorded by the sensor reflect the operating state of bearings, and extracting recognizable features effectively from them has become a hot issue in fault diagnosis. Currently, signal processing based filtering methods have emerged as a popular approach for extracting fault-related features. However, conventional filters based on specified assumptions and theoretical models have limited adaptability to multiple types of bearings under different operating conditions, which can significantly impact the diagnostic results. Given this, a data-driven Adaptive Class (AdaClass) filter is proposed to extract the response characteristics of different categories within the latent space. The filter details are obtained by statistically analyzing the mean vectors of each class samples in the reconstructed feature subspaces. Notably, the latent feature space is mapped by linear operators LDA and class-wise PCA, where the data has a more concise feature representation and a more distinct feature structure. The low-dimensional projection operations enhance the differential information among different categories, and reorganize the internal structure within the same category. Furthermore, a bearing fault diagnosis model is developed based on the AdaClass filter banks, utilizing one-step convolution to improve the efficiency of feature extraction. Experimental results show that the proposed method outperforms the competitors in terms of accuracy, time consumption, and noise resistance, especially for small sample scenarios.

show abstract

Advanced Signal Processing Methods for Condition Monitoring

Cited by 26 publications

References 113 publications

Analysis of Factors Affecting Electric Power Quality: PLS-SEM and Deep Learning Neural Network Analysis

Analysis of Factors Affecting Electric Power Quality: PLS-SEM and Deep Learning Neural Network Analysis

An In-Depth Study of Vibration Sensors for Condition Monitoring

AdaClass filter and its application in bearing fault diagnosis

Contact Info

Product

Resources

About