A Systematic Simulation-Based Multi-Criteria Decision-Making Approach for the Evaluation of Semi–Fully Flexible Machine System Process Parameters

Current manufacturing system health management is of prime importance due to the emergence of recent cost-effective and -efficient prognostics and diagnostics capabilities. This paper investigates the most used performance measures viz. Throughput Rate, Throughput Time, System Use, Availability, Average Stay Time, and Maximum Stay Time as alternatives that are responsible for the diagnostics of manufacturing systems during real-time disruptions. We have considered four different configurations as criteria on which to test with the proposed integrated MCDM (Multi-Criteria Decision-Making)-TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)-based simulation approach. The main objective of this proposed model is to improve the performance of semi–fully flexible systems and to maximize the production rate by ranking the parameters from most influenced to least. In this study, first, the performance of the considered process parameters are analyzed using a simulation approach, and furthermore the obtained results are validated using real-time experimental results. Thereafter, using an Entropy method, the weights of each parameter are identified and then the MCDM-based TOPSIS is applied to rank the parameters. The results show that Throughput tTme is the most affected parameter and that Availability, average stay time, and max stay time are least affected in the case of no breakdown of machine condition. Similarly, Throughput Time is the most affected parameter and Maximum Stay Time is the least affected parameter in the case of the breakdown of machine condition. Finally, the rankings from the TOPSIS method are compared with the PROMETHEE method rankings. The results demonstrate the ability to understand system behavior in both normal and uncertain conditions.

Analisis Pengendalian Mutu Kadar Air Teh Hitam pada Industri Pengolahan Teh

Quality control is intended to maintain and improve quality and maintain the safety of the products produced. This study uses Statistical Process Control by using several tools such as check sheets, control charts and fishbone diagrams to determine the cause of the decline in quality in tea with the aim that the next process can minimize the level of product quality decline. This study aims to determine the dominant cause of the decline in tea quality when viewed from the water content in tea. Based on the results of the study, it can be seen that the dominant cause of the decline in tea quality is the highwater content of dry tea. This type of deterioration can be caused by human error and other factors such as machine condition, raw materials and process monitoring.

Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Using Classification-Based Methods

The task of unsupervised anomalous sound detection (ASD) is challenging for detecting anomalous sounds from a large audio database without any annotated anomalous training data. Many unsupervised methods were proposed, but previous works have confirmed that the classification-based models far exceeds the unsupervised models in ASD. In this paper, we adopt two classification-based anomaly detection models: (1) Outlier classifier is to distinguish anomalous sounds or outliers from the normal; (2) ID classifier identifies anomalies using both the confidence of classification and the similarity of hidden embeddings. We conduct experiments in task 2 of DCASE 2020 challenge, and our ensemble method achieves an averaged area under the curve (AUC) of 95.82% and averaged partial AUC (pAUC) of 92.32%, which outperforms the state-of-the-art models.

Unsupervised Detection of Anomalous Sound for Machine Condition Monitoring using Fully Connected U-Net

Anomaly detection in the sound from machines is an important task in machine monitoring. An autoencoder architecture based on the reconstruction error using a log-Mel spectrogram feature is a conventional approach for this domain. However, because of the non-stationary nature of some sounds from the target machine, such a conventional approach does not perform well in those circumstances. In this paper, we propose a novel approach regarding the choice of used features and a new auto-encoder architecture. We created the Mixed Feature, which is a mixture of different sound representations, and a new deep learning method called Fully-Connected U-Net, a form of autoencoder architecture. With experiments on the same dataset as the baseline system, using the same architecture for all types of machines, the experimental results showed that our methods outperformed the baseline system in terms of the AUC and pAUC evaluation metrics. The optimized model achieved 83.38% AUC and 64.51% pAUC on average overall machine types on the developed dataset and outperformed the published baseline by 13.43% AUC and 8.13% pAUC.

Intelligent Condition Monitoring Models For Rotating

Condition-based maintenance (CBM) is a maintenance strategy that reduces equipment downtime, production loss, and maintenance cost based on the changes in machine condition (e.g., changes in vibration, power usage, operating performance, temperatures, noise levels, chemical composition, and debris content). A newly developed condition monitoring model (CMM) is developed based on Bayesian decision theory, which takes vibration signals from a rotating machine and classifies them to either the normal or abnormal state. A conditional risk function is defined, which is calculated based on a loss table and the posterior probabilities. Using the conditional risk funciton, the machine condition can be classified to either the normal or abnormal condition. The developed model can efficiently avoid unnecessary maintenance and take timely actions through analyzing the received vibration signals from the machine. However, the vibration signals sometimes may not be sensed, transmitted, or received precisely due to unexpected situations. Therefore, a fuzzy Bayesian model for condition monitoring of a system is proposed. A program is coded in visual basic to run the models. Illustrative examples are demonstrated to present the application of both models.

Intelligent Condition Monitoring Models For Rotating

Condition-based maintenance (CBM) is a maintenance strategy that reduces equipment downtime, production loss, and maintenance cost based on the changes in machine condition (e.g., changes in vibration, power usage, operating performance, temperatures, noise levels, chemical composition, and debris content). A newly developed condition monitoring model (CMM) is developed based on Bayesian decision theory, which takes vibration signals from a rotating machine and classifies them to either the normal or abnormal state. A conditional risk function is defined, which is calculated based on a loss table and the posterior probabilities. Using the conditional risk funciton, the machine condition can be classified to either the normal or abnormal condition. The developed model can efficiently avoid unnecessary maintenance and take timely actions through analyzing the received vibration signals from the machine. However, the vibration signals sometimes may not be sensed, transmitted, or received precisely due to unexpected situations. Therefore, a fuzzy Bayesian model for condition monitoring of a system is proposed. A program is coded in visual basic to run the models. Illustrative examples are demonstrated to present the application of both models.