1,721,008 research outputs found
Unsupervised classification of multichannel profile data using PCA: An application to an emission control system
No full textModern sensing technologies have facilitated real-time data collection for process monitoring and fault diagnosis in several research fields of industrial engineering. The challenges associated with diagnosis of multichannel (multiple) profiles are yet to be addressed in the literature. Motivated by an application of fault diagnosis of an emission control system, this paper proposes an approach for efficient and interpretable modeling of multichannel profile data in high-dimensional spaces. The method is based on unsupervised classification of multichannel profile data provided by several sensors related to a fault event. The final goal is to isolate fault events in a restricted number of clusters (scenarios), each one described by a reference pattern. This can provide practitioners with useful information to support the diagnosis and to find root cause. Two multilinear extensions of principal component analysis (PCA), which can analyze the multichannel profiles without unfolding the original data set, are investigated and compared to regular PCA applied to vectors generated by unfolding the original data set. The effectiveness of multilinear extensions of PCA is demonstrated using an experimental campaign carried out on an emission control system. Results of unsupervised classification show that the multilinear extension of PCA may lead to a classification with better compactness and separation of clusters
Fault diagnosis by multisensor data: A data-driven approach based on spectral clustering and pairwise constraints
Full text linkThis paper deals with clustering based on feature selection of multisensor data in high-dimensional space. Spectral clustering algorithms are efficient tools in signal processing for grouping datasets sampled by multisensor systems for fault diagnosis. The effectiveness of spectral clustering stems from constructing an embedding space based on an affinity matrix. This matrix shows the pairwise similarity of the data points. Clustering is then obtained by determining the spectral decomposition of the Laplacian graph. In the manufacturing field, clustering is an essential strategy for fault diagnosis. In this study, an enhanced spectral clustering approach is presented, which is augmented with pairwise constraints, and that results in efficient identification of fault scenarios. The effectiveness of the proposed approach is described using a real case study about a diesel injection control system for fault detection
Finite Mixture Models for Clustering Auto-Correlated Sales Series Data Influenced by Promotions
Full text linkThe focus of the present paper is on clustering, namely the problem of finding distinct groups in a dataset so that each group consists of similar observations. We consider the finite mixtures of regression models, given their flexibility in modeling heterogeneous time series. Our study aims to implement a novel approach, which fits mixture models based on the spline and polynomial regression in the case of auto-correlated data, to cluster time series in an unsupervised machine learning framework. Given the assumption of auto-correlated data and the usage of exogenous variables in the mixture model, the usual approach of estimating the maximum likelihood parameters using the Expectation–Maximization (EM) algorithm is computationally prohibitive. Therefore, we provide a novel algorithm for model fitting combining auto-correlated observations with spline and polynomial regression. The case study of this paper consists of the task of clustering the time series of sales data influenced by promotional campaigns. We demonstrate the effectiveness of our method in a case study of 131 sales series data from a real-world company. Numerical outcomes demonstrate the efficacy of the proposed method for clustering auto-correlated time series. Despite the specific case study of this paper, the proposed method can be used in several real-world application fields
Evaluation of deep learning with long short-term memory networks for time series forecasting in supply chain management
Full text linkPerformance analysis and forecasting the evolution of complex systems are two challenging tasks in manufacturing. Time series data from complex systems capture the dynamic behaviors of the underlying processes. However, non-linear and non-stationary dynamics pose a major challenge for accurate forecasting. To overcome statistical complexities through analyzing time series, we approach the problem with deep learning methods. In this paper, we mainly focus on the long short-term memory (LSTM) networks for demand forecasts in supply chain management, where the future demand for a certain product is the basis for the respective replenishment systems. This study contributes to the literature by conducting experiments on real data to investigate the potential of using LSTM networks for final customer demand forecasting, and hence for increasing the overall value generated by a supply chain. Both forward LSTM and bidirectional LSTM (forward-backward) for short-and long-term demand prediction in supply chain management are considered in this study
Binary Gaussian Process classification of quality in the production of aluminum alloys foams with regular open cells
Full text linkAluminum alloys foams with homogeneous and regular open cells have been frequently proposed and used as support structures for catalytic applications. In this kind of application, the quality of produced metal foam assumes primary importance. This paper presents an application of a classifier algorithm to predict quality in the manufacturing process of aluminum alloy foams with homogeneous and regular open cells. A data analysis methodology of experimental data, which is based on Binary Gaussian Process Classification, is presented. The proposed method is a Bayesian classification method, which gets away from any assumptions about the relationship between process inputs (the geometric design parameters of the regular unit cells) and process output (probability to obtain defective foam). We demonstrate that the proposed methodology can provide an effective tool to derive a model for the prediction of quality. An investment casting process, via 3D printing of wax patterns, is considered throughout the paper. Despite this specific case study, the methodology can be exploited in different processes in which the assumptions of traditional statistical approaches could not be easily verified, e.g., additive manufacturing
A Framework For The Development Of Distributed Simulation Code Oriented To The Manufacturing Field
No full textUtilizing Mixture Regression Models for Clustering Time-Series Energy Consumption of a Plastic Injection Molding Process
Full text linkConsidering the issue of energy consumption reduction in industrial plants, we investigated a clustering method for mining the time-series data related to energy consumption. The industrial case study considered in our work is one of the most energy-intensive processes in the plastics industry: the plastic injection molding process. Concerning the industrial setting, the energy consumption of the injection molding machine was monitored across multiple injection molding cycles. The collected data were then analyzed to establish patterns and trends in the energy consumption of the injection molding process. To this end, we considered mixtures of regression models given their flexibility in modeling heterogeneous time series and clustering time series in an unsupervised machine learning framework. Given the assumption of autocorrelated data and exogenous variables in the mixture model, we implemented an algorithm for model fitting that combined autocorrelated observations with spline and polynomial regressions. Our results demonstrate an accurate grouping of energy-consumption profiles, where each cluster is related to a specific production schedule. The clustering method also provides a unique profile of energy consumption for each cluster, depending on the production schedule and regression approach (i.e., spline and polynomial). According to these profiles, information related to the shape of energy consumption was identified, providing insights into reducing the electrical demand of the plant
Online automatic anomaly detection for photovoltaic systems using thermography imaging and low rank matrix decomposition
No full textFaults occurred during the operational lifetime of photovoltaic (PV) systems can cause energy loss, system shutdown, as well as possible fire risks. Therefore, it is crucial to detect anomalies and faults to control the system’s performance and ensure its reliability. Comparing to traditional monitoring techniques based on an on-site visual inspection and/ or electrical measuring devices, the combination of drones and infrared thermography imaging evidently provides the means for faster and less expensive PV monitoring. However, the literature in this area lacks automatic and implementable algorithms for PV fault detection, particularly, using raw aerial thermography, with precise performance evaluation. The objective of this paper is, thus, to build a fully automatic online monitoring framework. We propose an analytical framework for online analysis of the raw video streams of aerial thermography. This framework integrates image processing and statistical machine learning techniques. We validate the effectiveness of the proposed framework and provide sufficient details to facilitate its implementation by practitioners. Two challenges hinder direct fault detection on raw PV images. One is that raw PV images often have non-smooth backgrounds that can impact the detection performance. This background needs to be removed before fault detection. However, this is a daunting task given the perspective of images. To deal with this challenge, we utilize the Transform Invariant Low-rank Textures (TILT) method to orthogonalize the perspective before applying edge detection to crop out the background and aligning the cropped images. The other issue is that the regular hot spots at the bottom edges of the solar panels are normal and should not be detected as anomalies. This makes the intensity-based detection method in the literature fail. These hot spots are part of the low-rank pattern of the image sequence. On the other hand, the hot spots caused by anomalies deviate from the normal low-rank pattern of the PV cells. Therefore, we propose a methodology that relies on Robust Principal Component Analysis (RPCA), which can separate sparse corrupted anomalous components from a low-rank background. The RPCA is applied to the PV images for simultaneous detection and isolation of anomalies. In addition to RPCA, we suggest a set of post-processing procedures for image denoising, and segmentation. The proposed algorithm is developed using 20 normal (with no anomalies) training samples and 100 test samples. The results showed that the algorithm successfully detects the anomalies with a recall of 0.80 and detects the significant anomalies with the maximum recall of 1. Our method outperforms two benchmark methods in terms of F1 score by 44.5% and 114.3%. The small number of false alarms is mostly due to irregular image patterns at the end of a PV array or an extreme non-orthogonal perspective. Since the number of false alarms is not large, it does not disrupt the inspection process, and they can easily be identified by an appraiser offline. The average computation time is 6.32 sec/image, which enables online automatic inspection of PV panels
On Integrating Time-Series Modeling with Long Short-Term Memory and Bayesian Optimization: A Comparative Analysis for Photovoltaic Power Forecasting
Full text linkFeatured Application: The objective of this study is to explore the potential advantages of combining statistical modeling with Long Short-Term Memory (LSTM) and Bayesian Optimization (BO) algorithms for time-series forecasting in the context of Photovoltaic Power Forecasting (PVPF) implementation with limited input information. Our analysis revealed that integrating these methods resulted in more accurate forecasting outcomes than using each method separately. The means of energy generation are rapidly progressing as production shifts from a centralized model to a fully decentralized one that relies on renewable energy sources. Energy generation is intermittent and difficult to control owing to the high variability in the weather parameters. Consequently, accurate forecasting has gained increased significance in ensuring a balance between energy supply and demand with maximum efficiency and sustainability. Despite numerous studies on this issue, large sample datasets and measurements of meteorological variables at plant sites are generally required to obtain a higher prediction accuracy. In practical applications, we often encounter the problem of insufficient sample data, which makes it challenging to accurately forecast energy production with limited data. The Holt–Winters exponential smoothing method is a statistical tool that is frequently employed to forecast periodic series, owing to its low demand for training data and high forecasting accuracy. However, this model has limitations, particularly when handling time-series analysis for long-horizon predictions. To overcome this shortcoming, this study proposes an integrated approach that combines the Holt–Winters exponential smoothing method with long short-term memory and Bayesian optimization to handle long-range dependencies. For illustrative purposes, this new method is applied to forecast rooftop photovoltaic production in a real-world case study, where it is assumed that measurements of meteorological variables (such as solar irradiance and temperature) at the plant site are not available. Through our analysis, we found that by utilizing these methods in combination, we can develop more accurate and reliable forecasting models that can inform decision-making and resource management in this field
A simulated annealing approach for scheduling of non-independent jobs on FSMP
No full textBlacksburg, Virginia US
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