A novel approach for quality control of automated production lines working under highly inconsistent conditions

Bono, Francesco Morgan; Radicioni, Luca; Cinquemani, S.

doi:10.1016/j.engappai.2023.106149

Cited by 17 publications

(5 citation statements)

References 28 publications

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“…However, this approach still only considers a single state of operation. A methodology is proposed for building a soft sensor for quality control purposes, especially when the production machinery being monitored is characterized by highly inconsistent working conditions [44]. This study focuses on the work area and utilizes a convolutional neural network (CNN) and RNN for modeling.…”

Section: Lstm-based Soft Sensormentioning

confidence: 99%

A Fault-Tolerant Soft Sensor Algorithm Based on Long Short-Term Memory Network for Uneven Batch Process

Liu,

Ni,

Wang

2024

Processes

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Batch processing is a widely utilized technique in the manufacturing of high-value products. Traditional methods for quality assessment in batch processes often lead to productivity and yield losses because of offline measurement of quality variables. The use of soft sensors enhances product quality and increases production efficiency. However, due to the uneven batch data, the variation in processing times presents a significant challenge for building effective soft sensor models. Moreover, sensor failures, exacerbated by the manufacturing environment, complicate the accurate modeling of process variables. Existing soft sensor approaches inadequately address sensor malfunctions, resulting in significant prediction inaccuracies. This study proposes a fault-tolerant soft sensor algorithm that integrates two Long Short-Term Memory (LSTM) networks. The algorithm focuses on modeling process variables and compensating for sensor failures using historical batch quality data. It introduces a novel method for converting quality variables into process rates to align uneven batch data. A case study on simulated penicillin production validates the superiority of the proposed algorithm over conventional methods, showing its capacity for precise endpoint detection and effectiveness in addressing the challenges of batch process quality assurance. This study offers a robust solution to the issues of soft sensor reliability and data variability in industrial manufacturing.

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Section: Lstm-based Soft Sensormentioning

confidence: 99%

A Fault-Tolerant Soft Sensor Algorithm Based on Long Short-Term Memory Network for Uneven Batch Process

Liu,

Ni,

Wang

2024

Processes

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“…In this study, we employ Precision (P), Recall (R), F1-Score [39], and Mean Average Precision (mAP) [40] as metrics to evaluate the performance of our model. These metrics allow for a comprehensive assessment of the model's detection effectiveness and accuracy.…”

Section: Evaluation Indicatorsmentioning

confidence: 99%

YOLO-GP: A Multi-Scale Dangerous Behavior Detection Model Based on YOLOv8

Liu,

Yu,

Chen

et al. 2024

Symmetry

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In recent years, frequent chemical production safety incidents in China have been primarily attributed to dangerous behaviors by workers. Current monitoring methods predominantly rely on manual supervision, which is not only inefficient but also prone to errors in complex environments and with varying target scales, leading to missed or incorrect detections. To address this issue, we propose a deep learning-based object detection model, YOLO-GP. First, we utilize a grouped pointwise convolutional (GPConv) module of symmetric structure to facilitate information exchange and feature fusion in the channel dimension, thereby extracting more accurate feature representations. Building upon the YOLOv8n model, we integrate the symmetric structure convolutional GPConv module and design the dual-branch aggregation module (DAM) and Efficient Spatial Pyramid Pooling (ESPP) module to enhance the richness of gradient flow information and the capture of multi-scale features, respectively. Finally, we develop a channel feature enhancement network (CFE-Net) to strengthen inter-channel interactions, improving the model’s performance in complex scenarios. Experimental results demonstrate that YOLO-GP achieves a 1.56% and 11.46% improvement in the mAP@.5:.95 metric on a custom dangerous behavior dataset and a public Construction Site Safety Image Dataset, respectively, compared to the baseline model. This highlights its superiority in dangerous behavior object detection tasks. Furthermore, the enhancement in model performance provides an effective solution for improving accuracy and robustness, promising significant practical applications.

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“…For this reason, a resampling procedure based on the longest signal available is applied. 16,17 In this way, all the signals will have a number of samples equal to the ones in the longest acquisition (16360 samples). The last step concerns the data scaling by means of a max-min scaling technique.…”

Section: Signal-based Approachmentioning

confidence: 99%

Overcoming strain gauges limitation in the estimation of train load passing on a bridge through deep learning

Radicioni

Bono

Benedetti

et al. 2023

NDE 4.0, Predictive Maintenance, Communication, and Energy Systems: The Digital Transformation of NDE

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The estimation of trains weight could be useful under certain circumstances. For instance, in the field of structural health monitoring, some considerations can be derived from the evaluation of the load spectrum that an infrastructure has to withstand in its lifetime. One approach to estimate the train weight is based on the use of strain gauges mounted on the rail. The procedure allows to associate the local deformations with the load on an axle. However, strain gauges present several limitations: they are regarded as delicate sensors, and their replacement is burdensome and time-consuming. Moreover, their life is usually short when subjected to weathering and numerous load cycles. For these reasons, this paper proposes a novel methodology that relies on the use of more robust sensors mounted on a bridge structure for the estimation of the train load, alongside other information, such as the number of axles, the train speed, and the train class. The idea consists in the estimation of the train load starting from a network of sensors mounted on a bridge. A deep learning model is particularly suitable to achieve this task. The sensors network must consist of robust and easy-to-replace transducers (such as velocimeters mounted on the bridge structure). In this way, when the strain gauges are removed, the system is still able to estimate the loads passing on the bridge.

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A novel approach for quality control of automated production lines working under highly inconsistent conditions

Cited by 17 publications

References 28 publications

A Fault-Tolerant Soft Sensor Algorithm Based on Long Short-Term Memory Network for Uneven Batch Process

A Fault-Tolerant Soft Sensor Algorithm Based on Long Short-Term Memory Network for Uneven Batch Process

YOLO-GP: A Multi-Scale Dangerous Behavior Detection Model Based on YOLOv8

Overcoming strain gauges limitation in the estimation of train load passing on a bridge through deep learning

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