Advances in Machine Learning Detecting Changeover Processes in Cyber Physical Production Systems

The performance indicator, Overall Equipment Effectiveness (OEE), is one of the most important ones for production control, as it merges information of equipment usage, process yield, and product quality. The determination of the OEE is oftentimes not transparent in companies, due to the heterogeneous data sources and manual interference. Furthermore, there is a difference in present guidelines to calculate the OEE. Due to a big amount of sensor data in Cyber Physical Production Systems, Machine Learning methods can be used in order to detect several elements of the OEE by a trained model. Changeover time is one crucial aspect influencing the OEE, as it adds no value to the product. Furthermore, changeover processes are fulfilled manually and vary from worker to worker. They always have their own procedure to conduct a changeover of a machine for a new product or production lot. Hence, the changeover time as well as the process itself vary. Thus, a new Machine Learning based concept for identification and characterization of machine set-up actions is presented. Here, the issue to be dealt with is the necessity of human and machine interaction to fulfill the entire machine set-up process. Because of this, the paper shows the use case in a real production scenario of a small to medium size company (SME), the derived data set, promising Machine Learning algorithms, as well as the results of the implemented Machine Learning model to classify machine set-up actions.

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Model-Based Diagnosis for Cyber-Physical Production Systems Based on Machine Learning and Residual-Based Diagnosis Models

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33012727 ◽

2019 ◽

Vol 33 ◽

pp. 2727-2735 ◽

Cited By ~ 4

Author(s):

Andreas Bunte ◽

Benno Stein ◽

Oliver Niggemann

Keyword(s):

Machine Learning ◽

Production Systems ◽

Quantitative Model ◽

Machine Learning Algorithms ◽

Seamless Integration ◽

Model Based ◽

Novel Approach ◽

Qualitative Models ◽

Easy Integration ◽

Cyber Physical Production Systems

This paper introduces a novel approach to Model-Based Diagnosis (MBD) for hybrid technical systems. Unlike existing approaches which normally rely on qualitative diagnosis models expressed in logic, our approach applies a learned quantitative model that is used to derive residuals. Based on these residuals a diagnosis model is generated and used for a root cause identification. The new solution has several advantages such as the easy integration of new machine learning algorithms into MBD, a seamless integration of qualitative models, and a significant speed-up of the diagnosis runtime. The paper at hand formally defines the new approach, outlines its advantages and drawbacks, and presents an evaluation with real-world use cases.

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A Deep Learning Model for Predictive Maintenance in Cyber-Physical Production Systems Using LSTM Autoencoders

Sensors ◽

10.3390/s21030972 ◽

2021 ◽

Vol 21 (3) ◽

pp. 972

Author(s):

Xanthi Bampoula ◽

Georgios Siaterlis ◽

Nikolaos Nikolakis ◽

Kosmas Alexopoulos

Keyword(s):

Deep Learning ◽

Real World ◽

Short Term Memory ◽

Production Systems ◽

Learning Algorithm ◽

Remaining Useful Life ◽

Machine Learning Algorithms ◽

Sensor Data ◽

Maintenance Activities ◽

Cyber Physical Production Systems

Condition monitoring of industrial equipment, combined with machine learning algorithms, may significantly improve maintenance activities on modern cyber-physical production systems. However, data of proper quality and of adequate quantity, modeling both good operational conditions as well as abnormal situations throughout the operational lifecycle, are required. Nevertheless, this is difficult to acquire in a non-destructive approach. In this context, this study investigates an approach to enable a transition from preventive maintenance activities, that are scheduled at predetermined time intervals, into predictive ones. In order to enable such approaches in a cyber-physical production system, a deep learning algorithm is used, allowing for maintenance activities to be planned according to the actual operational status of the machine and not in advance. An autoencoder-based methodology is employed for classifying real-world machine and sensor data, into a set of condition-related labels. Real-world data collected from manufacturing operations are used for training and testing a prototype implementation of Long Short-Term Memory autoencoders for estimating the remaining useful life of the monitored equipment. Finally, the proposed approach is evaluated in a use case related to a steel industry production process.

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Micro Clustering Methodology for Document Objects using Deep Learning Techniques

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.b3716.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 1546-1552

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Recurrent Neural Network ◽

Large Data ◽

Heterogeneous Data ◽

Machine Learning Algorithms ◽

Volume Data ◽

Data Set ◽

Clustering And Classification

Large data clustering and classification is a very challenging task in data mining. Various machine learning and deep learning systems have been proposed by many researchers on a different dataset. Data volume, data size and structure of data may affect the time complexity of the system. This paper described a new document object classification approach using deep learning (DL) and proposed a recurrent neural network (RNN) for classification with a micro-clustering approach.TF-IDF and a density-based approach are used to store the best features. The plane work used supervised learning method and it extracts features set called as BK of the desired classes. once the training part completed then proceeds to figure out the particular test instances with the help of the planned classification algorithm. Recurrent Neural Network categorized the particular test object according to their weights. The system can able to work on heterogeneous data set and generate the micro-clusters according to classified results. The system also carried out experimental analysis with classical machine learning algorithms. The proposed algorithm shows higher accuracy than the existing density-based approach on different data sets.

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The CRI-model: A domain-independent taxonomy for non-conformance between observed and specified behaviour

Computer Science and Information Systems ◽

10.2298/csis180126034h ◽

2018 ◽

Vol 15 (3) ◽

pp. 705-731

Author(s):

Christopher Haubeck ◽

Alexander Pokahr ◽

Kim Reichert ◽

Till Hohenberger ◽

Winfried Lamersdorf

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Statistical Methods ◽

Production Systems ◽

Large Data ◽

Data Set ◽

Cloud Systems ◽

Cyber Physical Production Systems ◽

Distributed Cloud ◽

Domain Independent

Anomaly detection is the process of identifying nonconforming behaviour. Many approaches from machine learning to statistical methods exist to detect behaviour that deviate from its norm. These non-conformances of specifications can stem from failures in the system or undocumented changes of the system during its evolution. However, no generic solutions exist for classifying and identifying these non-conformances. In this paper, we present the CRI-Model (Cause, Reaction, Impact), which is a taxonomy based on a study of anomaly types in the literature, an analysis of system outages in major cloud companies and evolution scenarios which describe and implement changes in Cyber-Physical Production Systems. The goal of the taxonomy is to be usable for different objectives like discover gaps in the detection process, determine components most often affected by a particular anomaly type or describe system evolution. While the dimensions of the taxonomy are fixed, the categories can be adapted to different domains. We show and validate the applicability of the taxonomy to distributed cloud systems using a large data set of anomaly reports and cyber-physical production systems by categorizing common changes of an evolution benchmarking plant.

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Automatic Identification of Upper Extremity Rehabilitation Exercise Type and Dose Using Body-Worn Sensors and Machine Learning: A Pilot Study

Digital Biomarkers ◽

10.1159/000516619 ◽

2021 ◽

pp. 158-166

Author(s):

Noah Balestra ◽

Gaurav Sharma ◽

Linda M. Riek ◽

Ania Busza

Keyword(s):

Machine Learning ◽

Upper Extremity ◽

Sensor Data ◽

Inpatient Setting ◽

Accelerometer Data ◽

Data Set ◽

Machine Learning Classification ◽

Exercise Type ◽

Exercise Dose ◽

Rehabilitation Exercises

Background: Prior studies suggest that participation in rehabilitation exercises improves motor function poststroke; however, studies on optimal exercise dose and timing have been limited by the technical challenge of quantifying exercise activities over multiple days. Objectives: The objectives of this study were to assess the feasibility of using body-worn sensors to track rehabilitation exercises in the inpatient setting and investigate which recording parameters and data analysis strategies are sufficient for accurately identifying and counting exercise repetitions. Methods: MC10 BioStampRC® sensors were used to measure accelerometer and gyroscope data from upper extremities of healthy controls (n = 13) and individuals with upper extremity weakness due to recent stroke (n = 13) while the subjects performed 3 preselected arm exercises. Sensor data were then labeled by exercise type and this labeled data set was used to train a machine learning classification algorithm for identifying exercise type. The machine learning algorithm and a peak-finding algorithm were used to count exercise repetitions in non-labeled data sets. Results: We achieved a repetition counting accuracy of 95.6% overall, and 95.0% in patients with upper extremity weakness due to stroke when using both accelerometer and gyroscope data. Accuracy was decreased when using fewer sensors or using accelerometer data alone. Conclusions: Our exploratory study suggests that body-worn sensor systems are technically feasible, well tolerated in subjects with recent stroke, and may ultimately be useful for developing a system to measure total exercise “dose” in poststroke patients during clinical rehabilitation or clinical trials.

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Prediction of Pest Insect Appearance Using Sensors and Machine Learning

Sensors ◽

10.3390/s21144846 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4846

Author(s):

Dušan Marković ◽

Dejan Vujičić ◽

Snežana Tanasković ◽

Borislav Đorđević ◽

Siniša Ranđić ◽

...

Keyword(s):

Machine Learning ◽

Relative Humidity ◽

Weather Conditions ◽

Daily Basis ◽

Machine Learning Algorithms ◽

Lower Percentage ◽

Timely Manner ◽

Proposed Model ◽

Set Up ◽

Accuracy Of Prediction

The appearance of pest insects can lead to a loss in yield if farmers do not respond in a timely manner to suppress their spread. Occurrences and numbers of insects can be monitored through insect traps, which include their permanent touring and checking of their condition. Another more efficient way is to set up sensor devices with a camera at the traps that will photograph the traps and forward the images to the Internet, where the pest insect’s appearance will be predicted by image analysis. Weather conditions, temperature and relative humidity are the parameters that affect the appearance of some pests, such as Helicoverpa armigera. This paper presents a model of machine learning that can predict the appearance of insects during a season on a daily basis, taking into account the air temperature and relative humidity. Several machine learning algorithms for classification were applied and their accuracy for the prediction of insect occurrence was presented (up to 76.5%). Since the data used for testing were given in chronological order according to the days when the measurement was performed, the existing model was expanded to take into account the periods of three and five days. The extended method showed better accuracy of prediction and a lower percentage of false detections. In the case of a period of five days, the accuracy of the affected detections was 86.3%, while the percentage of false detections was 11%. The proposed model of machine learning can help farmers to detect the occurrence of pests and save the time and resources needed to check the fields.

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Swarm Intelligence Optimization: An Exploration and Application of Machine Learning Technology

Journal of Intelligent Systems ◽

10.1515/jisys-2020-0084 ◽

2021 ◽

Vol 30 (1) ◽

pp. 460-469

Author(s):

Yinying Cai ◽

Amit Sharma

Keyword(s):

Machine Learning ◽

Swarm Intelligence ◽

Research Result ◽

Machine Learning Algorithms ◽

Learning Technology ◽

Data Set ◽

Rice Pests ◽

Machine Leaning ◽

Smart Agriculture ◽

Swarm Intelligence Optimization

Abstract In the agriculture development and growth, the efficient machinery and equipment plays an important role. Various research studies are involved in the implementation of the research and patents to aid the smart agriculture and authors and reviewers that machine leaning technologies are providing the best support for this growth. To explore machine learning technology and machine learning algorithms, the most of the applications are studied based on the swarm intelligence optimization. An optimized V3CFOA-RF model is built through V3CFOA. The algorithm is tested in the data set collected concerning rice pests, later analyzed and compared in detail with other existing algorithms. The research result shows that the model and algorithm proposed are not only more accurate in recognition and prediction, but also solve the time lagging problem to a degree. The model and algorithm helped realize a higher accuracy in crop pest prediction, which ensures a more stable and higher output of rice. Thus they can be employed as an important decision-making instrument in the agricultural production sector.

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Assessing biases, relaxing moralism: On ground-truthing practices in machine learning design and application

Big Data & Society ◽

10.1177/20539517211013569 ◽

2021 ◽

Vol 8 (1) ◽

pp. 205395172110135

Author(s):

Florian Jaton

Keyword(s):

Machine Learning ◽

William James ◽

A Priori ◽

Learning Algorithms ◽

Three Dimensional ◽

Ground Truth ◽

Machine Learning Algorithms ◽

Ground Truthing ◽

Set Up ◽

The Moment

This theoretical paper considers the morality of machine learning algorithms and systems in the light of the biases that ground their correctness. It begins by presenting biases not as a priori negative entities but as contingent external referents—often gathered in benchmarked repositories called ground-truth datasets—that define what needs to be learned and allow for performance measures. I then argue that ground-truth datasets and their concomitant practices—that fundamentally involve establishing biases to enable learning procedures—can be described by their respective morality, here defined as the more or less accounted experience of hesitation when faced with what pragmatist philosopher William James called “genuine options”—that is, choices to be made in the heat of the moment that engage different possible futures. I then stress three constitutive dimensions of this pragmatist morality, as far as ground-truthing practices are concerned: (I) the definition of the problem to be solved (problematization), (II) the identification of the data to be collected and set up (databasing), and (III) the qualification of the targets to be learned (labeling). I finally suggest that this three-dimensional conceptual space can be used to map machine learning algorithmic projects in terms of the morality of their respective and constitutive ground-truthing practices. Such techno-moral graphs may, in turn, serve as equipment for greater governance of machine learning algorithms and systems.

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