Quality Control of the Continuous Hot Pressing Process of Medium Density Fiberboard Using Fuzzy Failure Mode and Effects Analysis

Lv, Yunlei; Liu, Yaqiu; Jing, Weipeng; Woźniak, Marcin; Damaševičius, Robertas; Scherer, Rafał; Wei, Wei

doi:10.3390/app10134627

Cited by 20 publications

(13 citation statements)

References 31 publications

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“…Productivity includes labor productivity, capital output rate, and consumption rate of production materials. Among the abovementioned factors, the input of science and technology, factor productivity, and education is closely related [24][25][26][27]. erefore, the effect of education on economic growth can be summarized into the following aspects.…”

Section: E Effect Of Education On Economic Growthmentioning

confidence: 99%

[Retracted] Forecast and Evaluation of Educational Economic Contribution Based on Fuzzy Neural Network

Liu

2021

Complexity

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According to the theory that the degree of education of workers in educational economics has a certain positive relationship with social labor productivity, the fuzzy system and neural network modeling mechanism are used to establish the fuzziness of laborers’ education level to social productivity (per capita national income). This article combines fuzzy theory and neural network theory to construct an empirical model for the analysis of the contribution of education economy and conduct an empirical analysis of statistical data from 2010 to 2020. Analysis shows that there is a great correlation between per capita years of education and per capita GDP, especially the number of college students per million people has a greater correlation with per capita GDP. This fully confirms that economic growth is increasingly dependent on education, especially higher education. The main body of this article is the improvement of the measurement model and the calculation of the contribution of our country’s education to economic growth using the fuzzy neural network measurement model. The final empirical conclusion shows that education has a significant role in promoting the development of our country’s economy.

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Section: E Effect Of Education On Economic Growthmentioning

confidence: 99%

[Retracted] Forecast and Evaluation of Educational Economic Contribution Based on Fuzzy Neural Network

Liu

2021

Complexity

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“…The technique has recently received further attention due to the development of the Internet of Things (IoT) and the following explosive growth of big data and to rapid improvement of machine learning techniques, especially deep learning, in the last decade. Anomaly detection is recognized as one of the essential techniques in an application for preventive maintenance of the industrial machine [2] as well as for predictive maintenance of useful life (or time to failure) [3] and quality control [4]. Anomaly detection of industrial machinery relies on various diagonal data from equipped sensors, such as temperature, pressure, electric current, vibration, and sound, to name a few.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Anomaly Detection of Mechanical Failures in Noisy Real-Life Factory Environments

2021

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Anomaly detection without employing dedicated sensors for each industrial machine is recognized as one of the essential techniques for preventive maintenance and is especially important for factories with low automatization levels, a number of which remain much larger than autonomous manufacturing lines. We have based our research on the hypothesis that real-life sound data from working industrial machines can be used for machine diagnostics. However, the sound data can be contaminated and drowned out by typical factory environmental sound, making the application of sound data-based anomaly detection an overly complicated process and, thus, the main problem we are solving with our approach. In this paper, we present a noise-tolerant deep learning-based methodology for real-life sound-data-based anomaly detection within real-world industrial machinery sound data. The main element of the proposed methodology is a generative adversarial network (GAN) used for the reconstruction of sound signal reconstruction and the detection of anomalies. The experimental results obtained in the Malfunctioning Industrial Machine Investigation and Inspection (MIMII) show the superiority of the proposed methodology over baseline approaches based on the One-Class Support Vector Machine (OC-SVM) and the Autoencoder–Decoder neural network. The proposed schematics using the unscented Kalman Filter (UKF) and the mean square error (MSE) loss function with the L2 regularization term showed an improvement of the Area Under Curve (AUC) for the noisy pump data of the pump.

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“…Currently, in the literature, various approaches are used in the evaluation of risk analysis. One of the alternatives is fuzzy logic, which was used by Liu HC et al [22], S. Bahrebar et al [23], S. Liu et al [24], Y. Lv et al [25], and G. Filo et al [26]. The weakness of the classical RPN model was also noticed by the industry and recently was replaced by action-priority [27].…”

Section: Introductionmentioning

confidence: 99%

A Matrix FMEA Analysis of Variable Delivery Vane Pumps

2021

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Hydraulic systems are widely used in the aeronautic, machinery, and energy industries. The functions that these systems perform require high reliability, which can be achieved by examining the causes of possible defects and failures and by taking appropriate preventative measures. One of the most popular methods used to achieve this goal is FMEA (Failure Modes and Effects Analysis), the foundations of which were developed and implemented in the early 1950s. It was systematized in the following years and practically implemented. It has also been standardized and implemented as one of the methods of the International Organization for Standardization (ISO) 9000 series standards on quality assurance and management. Apart from wide application, FMEA has a number of weaknesses, which undoubtedly include risk analysis based on the RPN (Risk Priority Number), which is evaluated as a product of severity, occurrence, and detection. In recent years, the risk analysis has been very often replaced by fuzzy logic. This study proposes the use of matrix analysis and statistical methods for performing simplified RCA (Root Cause Analysis) and for classification potential failures for a variable delivery vane pump. The presented methodology is an extension of matrix FMEA and allows for prioritizing potential failures and their causes in relation to functions performed by pump components, the end effects, and the defined symptoms of failure of the vane pump.

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Quality Control of the Continuous Hot Pressing Process of Medium Density Fiberboard Using Fuzzy Failure Mode and Effects Analysis

Cited by 20 publications

References 31 publications

[Retracted] Forecast and Evaluation of Educational Economic Contribution Based on Fuzzy Neural Network

[Retracted] Forecast and Evaluation of Educational Economic Contribution Based on Fuzzy Neural Network

Acoustic Anomaly Detection of Mechanical Failures in Noisy Real-Life Factory Environments

A Matrix FMEA Analysis of Variable Delivery Vane Pumps

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