Time-Series Forecasting in Smart Manufacturing Systems: An Experimental Evaluation of the State-of-the-art Algorithms

TSF is growing in various domains including manufacturing. Although numerous TSF algorithms have been developed recently, the validation and evaluation of algorithms hold substantial value for researchers and practitioners and are missing. This study aims to fill this gap by evaluating the SoTA TSF algorithms on thirteen manufacturing datasets, focusing on their applicability in manufacturing. Each algorithm was selected based on its TSF category to ensure a representative set of algorithms. The evaluation includes different scenarios to evaluate the models using two problem categories and two forecasting horizons. To evaluate the performance, the WAPE was calculated, and additional post hoc analyses were conducted to assess the significance of observed differences. Only algorithms with codes from open-source libraries were utilized, and no hyperparameter tuning was done. This allowed us to evaluate the algorithms as "out-of-the-box" solutions that can be easily implemented, ensuring their usability within the manufacturing by practitioners with limited technical knowledge. This aligns to facilitate the adoption of these techniques in smart manufacturing systems. Based on the results, transformer and MLP-based architectures demonstrated the best performance with MLP-based architecture winning the most scenarios. For univariate TSF, PatchTST emerged as the most robust, particularly for long-term horizons, while for multivariate problems, MLP-based architectures like N-HITS and TiDE showed superior results. The study revealed that simpler algorithms like XGBoost could outperform complex algorithms in certain tasks. These findings challenge the assumption that more sophisticated models produce better results. Additionally, the research highlighted the importance of computational resource considerations, showing variations in runtime and memory usage across different algorithms.

Time-Series Classification in Smart Manufacturing Systems: An Experimental Evaluation of State-of-the-Art Machine Learning Algorithms

Manufacturing is gathering extensive amounts of diverse data, thanks to the growing number of sensors and rapid advances in sensing technologies. Among the various data types available in SMS settings, time-series data plays a pivotal role. Hence, TSC emerges is crucial in this domain. The objective of this study is to fill this gap by providing a rigorous experimental evaluation of the SoTA ML and DL algorithms for TSC tasks in manufacturing and industrial settings. We first explored and compiled a comprehensive list of more than 92 SoTA algorithms from both TSC and manufacturing literature. Following, we selected the 36 most representative algorithms from this list. To evaluate their performance across various manufacturing classification tasks, we curated a set of 22 manufacturing datasets, representative of different characteristics that cover diverse manufacturing problems. Subsequently, we implemented and evaluated the algorithms on the manufacturing benchmark datasets, and analyzed the results for each dataset. Based on the results, ResNet, DrCIF, InceptionTime, and ARSENAL are the top-performing algorithms, boasting an average accuracy of over 96.6% across all 22 manufacturing TSC datasets. These findings underscore the robustness, efficiency, scalability, and effectiveness of convolutional kernels in capturing temporal features in time-series data, as three out of the top four performing algorithms leverage these kernels for feature extraction. Additionally, LSTM, BiLSTM, and TS-LSTM algorithms deserve recognition for their effectiveness in capturing features within time-series data using RNN-based structures.

Multivariate time series classification with dual attention network

One of the topics in machine learning that is becoming more and more relevant is multivariate time series classification. Current techniques concentrate on identifying the local important sequence segments or establishing the global long-range dependencies. They frequently disregard the merged data from both global and local features, though. Using dual attention, we explore a novel network (DA-Net) in this research to extract local and global features for multivariate time series classification. The two distinct layers that make up DA-Net are the Squeeze-Excitation Window Attention (SEWA) layer and the Sparse Self-Attention within Windows (SSAW) layer. DA- Net can mine essential local sequence fragments that are necessary for establishing global long-range dependencies based on the two expanded layers.

A Genetic Algorithm Meta-Heuristic for a Generalized Quadratic Assignment Problem

The generalized quadratic assignment problem (GQAP) is one of the hardest problems to solve in the operations research area. The GQAP addressed in this work is defined as the task of minimizing the assignment and transportation costs of assigning a set of facilities to a set of locations. The facilities have different space requirements, and the locations have different space capacities. Multiple facilities can be assigned to each location if the space capacity is not violated. In this work, three instances of GQAP in different situations are presented. Then, a genetic algorithm is developed to solve the GQAP instances. Finally, the local neighborhood search with the steepest descend strategy is constructed and applied to the final solution obtained by the GA, and the final solution is compared with the best solution found by MPL/CPLEX software and reference papers. The results show that the developed GA heuristic is effective for solving the GQAP.

Time-Series Pattern Recognition in Smart Manufacturing Systems: A Literature Review and Ontology

Since the inception of Industry 4.0 in 2012, emerging technologies have enabled the acquisition of vast amounts of data from diverse sources such as machine tools, robust and affordable sensor systems with advanced information models, and other sources within Smart Manufacturing Systems (SMS). As a result, the amount of data that is available in manufacturing settings has exploded, allowing data-hungry tools such as Artificial Intelligence (AI) and Machine Learning (ML) to be leveraged. Time-series analytics has been successfully applied in a variety of industries, and that success is now being migrated to pattern recognition applications in manufacturing to support higher quality products, zero defect manufacturing, and improved customer satisfaction. However, the diverse landscape of manufacturing presents a challenge for successfully solving problems in industry using time-series pattern recognition. The resulting research gap of understanding and applying the subject matter of time-series pattern recognition in manufacturing is a major limiting factor for adoption in industry. The purpose of this paper is to provide a structured perspective of the current state of time-series pattern recognition in manufacturing with a problem-solving focus. By using an ontology to classify and define concepts, how they are structured, their properties, the relationships between them, and considerations when applying them, this paper aims to provide practical and actionable guidelines for application and recommendations for advancing time-series analytics.