Process Monitoring System Research Articles

Data fusion analysis in the powder-bed fusion AM process monitoring by Dempster-Shafer evidence theory

PurposeThis study aims to develop a data fusion method for powder-bed fusion (PBF) process monitoring based on process image information. The data fusion method can help improve process condition identification performance, which can provide guidance for further PBF process monitoring and control system development.Design/methodology/approachDesign of reliable process monitoring systems is an essential approach to solve PBF built quality. A data fusion framework based on support vector machine (SVM), convolutional neural network (CNN) and Dempster-Shafer (D-S) evidence theory are proposed in the study. The process images which include the information of melt pool, plume and spatters were acquired by a high-speed camera. The features were extracted based on an appropriate image processing method. The three feature vectors corresponding to the three objects, respectively, were used as the inputs of SVM classifiers for process condition identification. Moreover, raw images were also used as the input of a CNN classifier for process condition identification. Then, the information fusion of the three SVM classifiers and the CNN classifier by an improved D-S evidence theory was studied.FindingsThe results demonstrate that the sensitivity of information sources is different for different condition identification. The feature fusion based on D-S evidence theory can improve the classification performance, with feature fusion and classifier fusion, the accuracy of condition identification is improved more than 20%.Originality/valueAn improved D-S evidence theory is proposed for PBF process data fusion monitoring, which is promising for the development of reliable PBF process monitoring systems.

Biomass Combustion Control in Small and Medium-Scale Boilers Based on Low Cost Sensing the Trend of Carbon Monoxide Emissions

The article deals with the possibility of efficient control of small and medium-scale biomass-fired boilers by implementing low-cost sensors to sense the trend of carbon monoxide emissions into control of the biomass combustion process. Based on the theoretical analysis, a principle block diagram of the process control system was designed for the possibility of providing near-optimal control of the biomass combustion regardless of its quality parameters. A cost-effective hardware solution to obtain the dependence of CO emissions on O2 concentration in flue gas during combustion and new control algorithms was implemented into the process control and monitoring system of the biomass-fired boilers to test them in the real operation. A description of the designed control system, a data analysis of the monitored values and their impact on combustion process, and some results of the implemented control of the real biomass combustion process are presented in the article.

Transfer of Process References between Machine Tools for Online Tool Condition Monitoring

Process and tool condition monitoring systems are a prerequisite for autonomous production. One approach to monitoring individual parts without complex cutting simulations is the transfer of knowledge among similar monitoring scenarios. This paper introduces a novel monitoring method which transfers monitoring limits for process signals between different machine tools. The method calculates monitoring limits statistically from cutting processes carried out on one or more similar machines. The monitoring algorithm aims to detect general process anomalies online. Experiments comprise face-turning operations at five different lathes, four of which were of the same model. Results include the successful transfer of monitoring limits between machines of the same model for the detection of material anomalies. In comparison to an approach based on dynamic time warping (DTW) and density-based spatial clustering of applications with noise (DBSCAN), the new method showed fewer false alarms and higher detection rates. However, for the transfer between different models of machines, the successful application of the new method is limited. This is predominantly due to limitations of the employed process component isolation and differences between machine models in terms of signal properties as well as execution speed.

Process Monitoring in Ultrasonic Metal Welding of Lithium Batteries by Power Signals

Abstract Ultrasonic metal welding is one of the key technologies in manufacturing lithium batteries, and the welding quality directly determines the battery performance. Therefore, an online welding process monitoring system is critical in identifying abnormal welding processes, detecting defects, and improving battery quality. Traditionally, the peak welding power is used to indicate abnormal process signals in welding process monitoring systems. However, since various factors have complex impacts on the electric power signals of ultrasonic welding processes, the peak power is inadequate to detect different types of welding defects. Therefore, a signal pattern matching method is proposed in this study, which is based on the electric power signal during the entire welding process and thus is capable of identifying abnormal welding processes in various conditions. The proposed method adopts isometric transformation and homogenization as signal pretreatment methods, and Euclidean distance is used to calculate the similarity metric for signal matching. The effectiveness and robustness of the proposed method are experimentally validated under different abnormal welding conditions.

Unsupervised machine learning techniques for fault detection and diagnosis in nuclear power plants

Nuclear power plants have proved their importance in the energy sector by generating clean and uninterrupted energy over decades. Moreover, nuclear power plants (NPPs) are large-scale and complex systems with potential radioactive release risks. Hence, it is important to achieve the highest standards of safety by designing a reliable process monitoring system. Due to the massive amount of monitoring data that are collected and stored in modern NPPs, it is difficult to extract necessary information about the actual plant state in a timely and accurate manner. Machine learning techniques play a vital role as tools for data mining that help in decision-making and support the operators in the process industry. This paper presents an integrated framework for fault detection and diagnosis in NPPs using unsupervised machine learning techniques where there is no prior knowledge is required. In this framework, the faults are first detected using the principal component analysis (PCA) approach by different fault detection indices. Second, the multivariate contribution plots (MCPs) methods are used for fault diagnosis. The data collected from Personal Computer Transient Analyser (PCTRAN) simulation is utilized to demonstrate the efficiencies of all the discussed methods.

Production Tracking and Managementfor SMEs

The problems experienced by Jazzy Souvenir SMEs are in the form of obstacles in monitoring the souvenir production process and difficulties in calculating the number of souvenirs that have been completed. The purpose of this study was to determine the method of designing PROTIM (Production Tracking and Management) as a monitoring system for the production process to increase production capacity in Jazzy Souvenir SMEs. This study uses qualitative methods with research in the form of program implementation. The implementation of the student creativity program in the field of science and technology application was carried out for three months which was divided into three stages, the first was problem identification and literature study at partner locations. The next stage is software design with the team using video conferencing. While the last stage is socialization, application of tools as well as monitoring and evaluation at SMEs Jazzy Souvenir. The conclusion that can be drawn from this research is that to overcome the obstacles in monitoring the souvenir production process as well as the difficulty in calculating the number of souvenirs that have been completed is to use the PROTIM design method (Production Tracking and Management) as a production process monitoring system to increase production capacity in MSME Jazzy Souvenirs. The existence of this innovation and new technology is very useful for partners in overcoming the problem of increasing production capacity for Jazzy Souvenir SME partner employees.

Production Tracking and Managementfor SMEs

The problems experienced by Jazzy Souvenir SMEs are in the form of obstacles in monitoring the souvenir production process and difficulties in calculating the number of souvenirs that have been completed. The purpose of this study was to determine the method of designing PROTIM (Production Tracking and Management) as a monitoring system for the production process to increase production capacity in Jazzy Souvenir SMEs. This study uses qualitative methods with research in the form of program implementation. The implementation of the student creativity program in the field of science and technology application was carried out for three months which was divided into three stages, the first was problem identification and literature study at partner locations. The next stage is software design with the team using video conferencing. While the last stage is socialization, application of tools as well as monitoring and evaluation at SMEs Jazzy Souvenir. The conclusion that can be drawn from this research is that to overcome the obstacles in monitoring the souvenir production process as well as the difficulty in calculating the number of souvenirs that have been completed is to use the PROTIM design method (Production Tracking and Management) as a production process monitoring system to increase production capacity in MSME Jazzy Souvenirs. The existence of this innovation and new technology is very useful for partners in overcoming the problem of increasing production capacity for Jazzy Souvenir SME partner employees.

A defect detection system for wire arc additive manufacturing using incremental learning

In more recent times, research on various aspects of the Wire Arc Additive Manufacturing (WAAM) process has been conducted, and efforts into monitoring the WAAM process for defect identification have increased. Rapid and reliable monitoring of the WAAM process is a key development for the technology as a whole, as it will enable components produced by the process to be qualified to relevant standards and hence be deemed fit for use in applications such as those found in the aerospace or naval sectors. Intelligent algorithms provide inbuilt advantages in processing and analysing data, especially for the large data sets generated during the long manufacturing cycles. Interdisciplinary engineering (IDE) furnishes a concept integrating computer science and industrial system manufacturing engineering together to treat large amounts of process monitoring data. In this work, a WAAM process monitoring and defect detection system integrating intelligent algorithms is presented. The system monitors welding arc current and voltage signals produced by the WAAM process and makes use of a support vector machine (SVM) learning method to identify disturbances to the welding signal which indicate the presence of potential defects. The incremental machine learning models developed in this work are trained via statistical feature analysis of the welding signals and a novel quality metric that improves detection rates is also presented. The incremental learning approach provides an efficient means of detecting welding-based defects, as it does not require large quantities of data to be trained to an operational level (addressing a major drawback of other machine learning methods). A case study is presented to validate the developed system, results show that it was able to detect a set of defects with a success rate greater than 90% F1-score.

Business process monitoring system in supporting information technology governance

Information technology (IT) is essential in supporting an organization's business sustainability and growth, making it critically dependent on IT. Therefore, a focus on IT governance, consisting of leadership, organizational structure, and process ensuring that IT organization supports and expands the organizational strategies and goals is required. When the business supports the strategic significance of IT investment, the implementation of an IT strategy will lead to the adoption of an IT governance model. It will support and help the description of the benefit roles and responsibilities from IT systems and infrastructure. This paper aims to develop a business process monitoring system to support IT governance in improving user service and measuring organizational performance. The research method was the system development method with the Waterfall model. To measure the performance of the business process, the self-assessment method with performance matrix tools was applied. The study resulted in a business process monitoring system that can enhance the organization’s primary business process in services, supporting the said organization’s performance.

Towards Material-Batch-Aware Tool Condition Monitoring

In subtractive manufacturing, process monitoring systems are used to observe the manufacturing process, to predict maintenance actions and to suggest process optimizations. One challenge, however, is that the observable signals are influenced not only by the degradation of the cutting tool, but also by deviations in machinability among material batches. Thus it is necessary to first predict the respective material batch before making maintenance decisions. In this study, an approach is shown for batch-aware tool condition monitoring using feature extraction and unsupervised learning to analyze high-frequency control data in order to detect clusters of materials with different machinability, and subsequently optimize the respective manufacturing process. This approach is validated using cutting experiments and implemented as an edge framework.

Evolutionary Optimization of Machining Parameters Based on Surface Roughness in End Milling of Hot Rolled Steel.

Surface roughness measurements of machined parts are usually performed off-line after the completion of the machining operation. The objective of this work is to develop a surface roughness prediction method based on the processing of vibration signals during steel end milling operation performed on a vertical CNC machining center. The milling cuts were run under varying conditions (such as the spindle speed, feed rate, and depth of cut). This is a first step in the attempt to develop an online milling process monitoring system. The study presented here involves the analysis of vibration signals using statistical time parameters, frequency spectrum, and time-frequency wavelet decomposition. The analysis resulted in the extraction of 245 features that were used in the evolutionary optimization study to determine optimal cutting conditions based on the measured surface roughness of the milled specimen. Three feature selection methods were used to reduce the extracted feature set to smaller subsets, followed by binarization using two binarization methods. Three evolutionary algorithms—a genetic algorithm, particle swarm optimization and two variants, differential evolution and one of its variants, have been used to identify features that relate to the “best” surface finish measurements. These optimal features can then be related to cutting conditions (cutting speed, feed rate, and axial depth of cut). It is shown that the differential evolution and its variant performed better than the particle swarm optimization and its variants, and both differential evolution and particle swarm optimization perform better than the canonical genetic algorithm. Significant differences are found in the feature selection methods too, but no difference in performance was found between the two binarization methods.

Development and Testing of a Turning Process Monitoring System using Acoustic Emission

Studies on in-process measurement have suggested techniques for sensing tool wear and machine status in a machining center and turning center, for example by measuring cutting resistance. However, there appear to be no reports of effective uses of machine sensing in practical machine tools, perhaps due to problems such as limitations on the number and density of sensors and the possibility that attaching sensors may affect machine rigidity and thus processing quality. The purpose of this research is to develop a system that can monitor the state of the cutting process and maintain it in a normal or optimal state at all times, based on the acoustic emission (AE) method. To realize the development of such a system, it is very important to qualitatively and quantitatively evaluate the tool wear. This report describes a tool wear experiment that collected AE signals, cutting force data, and high-speed camera images during metal turning. The collected basic data are comprehensively evaluated and examined to determine the effectiveness of in-process measurement by the AE method. The results suggest that evaluating various AE parameters obtained from the AE original waveform is an effective parameter for monitoring tool wear and cutting conditions that affect product quality. The findings from the basic data obtained in this study were found to be useful information for the practical application of in-process measurement by the AE method using machine learning methods.

Evaluation of Clustering Techniques to Predict Surface Roughness during Turning of Stainless-Steel Using Vibration Signals.

In metal-cutting processes, the interaction between the tool and workpiece is highly nonlinear and is very sensitive to small variations in the process parameters. This causes difficulties in controlling and predicting the resulting surface finish quality of the machined surface. In this work, vibration signals along the major cutting force direction in the turning process are measured at different combinations of cutting speeds, feeds, and depths of cut using a piezoelectric accelerometer. The signals are processed to extract features in the time and frequency domains. These include statistical quantities, Fast Fourier spectral signatures, and various wavelet analysis extracts. Various feature selection methods are applied to the extracted features for dimensionality reduction, followed by applying several outlier-resistant unsupervised clustering algorithms on the reduced feature set. The objective is to ascertain if partitions created by the clustering algorithms correspond to experimentally obtained surface roughness data for specific combinations of cutting conditions. We find 75% accuracy in predicting surface finish from the Noise Clustering Fuzzy C-Means (NC-FCM) and the Density-Based Spatial Clustering Applications with Noise (DBSCAN) algorithms, and upwards of 80% accuracy in identifying outliers. In general, wrapper methods used for feature selection had better partitioning efficacy than filter methods for feature selection. These results are useful when considering real-time steel turning process monitoring systems.

Automated High-Speed S-Wave Velocity Tomographic System for Laboratory Geotechnical Engineering Process Monitoring – A Study on Pile Installation

Abstract This paper reports the innovative development of a flexible, multiple-layer, economical but robust, and automated high-speed shear wave (Vs) tomographic control and data acquisition system for process monitoring in the laboratory and the associated validation for monitoring the dynamic process of model pile installation. This tailor-made tomographic system consists of four major parts: (i) the control unit built with a microcontroller board, (ii) a signal generating unit, (iii) three layers of bender element sensing arrays (including 14 source and receiver bender elements per layer), and (iv) a signal receiving unit. The system allows for sampling rates up to 1 GS/s (20 MS/s were used in this study); the required total measurement time is 2.94 seconds (i.e., for 196 ray path measurements) to allow for dynamic process monitoring. The obtained time-lapse Vs tomographic images during pile installation show that Vs first gradually increases when the pile toe is approaching the sensing layer and gradually decreases afterward as the pile toe penetrates through the layer. The validity of the Vs tomographic images is supported by similar responses in variations of the circumferential stress σc′ and radial stress σr′, measured by the tactile pressure sensors.

Assessing and improving measurability of process performance indicators based on quality of logs

The efficiency and effectiveness of business processes are usually evaluated by Process Performance Indicators (PPIs), which are computed using process event logs. PPIs can be insightful only when they are measurable, i.e., reliable. This paper proposes to define PPI measurability on the basis of the quality of the data in the process logs. Then, based on this definition, a framework for PPI measurability assessment and improvement is presented. For the assessment, we propose novel definitions of PPI accuracy, completeness, consistency, timeliness and volume that contextualise the traditional definitions in the data quality literature to the case of process logs. For the improvement, we define a set of guidelines for improving the measurability of a PPI. These guidelines may concern improving existing event logs, for instance through data imputation, implementation or enhancement of the process monitoring systems, or updating the PPI definitions. A case study in a large-sized institution is discussed to show the feasibility and the practical value of the proposed framework.

Real-Time Process Monitoring Based on Multivariate Control Chart for Anomalies Driven by Frequency Signal via Sound and Electrocardiography Cases

Recent developments in network technologies have led to the application of cloud computing and big data analysis to industrial automation. However, the automation of process monitoring still has numerous issues that need to be addressed. Traditionally, offline statistical processes are generally used for process monitoring; thus, problems are often detected too late. This study focused on the construction of an automated process monitoring system based on sound and vibration frequency signals. First, empirical mode decomposition was combined with intrinsic mode functions to construct different sound frequency combinations and differentiate sound frequencies according to anomalies. Then, linear discriminant analysis (LDA) was adopted to classify abnormal and normal sound frequency signals, and a control line was constructed to monitor the sound frequency. In a case study, the proposed method was applied to detect abnormal sounds at high and low frequencies, and a detection accuracy of over 90% was realized. In another case study, the proposed method was applied to analyze electrocardiography signals and was similarly able to identify abnormal situations. Thus, the proposed method can be applied to real-time process monitoring and the detection of abnormalities with high accuracy in various situations.

A novel key performance indicator oriented process monitoring method based on multiple information extraction and support vector data description

AbstractAs a core part of modern chemical plants, key performance indicator oriented process monitoring and fault diagnosis systems have gradually made great contributions to guaranteeing process safety, improving product quality, and ensuring system reliability, which recently have received extensive attention and become one of the hot spots both in academic research and industrial application fields. Different from previous methods, a novel key performance indicator oriented process monitoring method is proposed in this paper, which fully mines and utilizes important time feature information hidden in the process data while considering the local process information. Firstly, a group of representative process variables with maximum key performance indicator information are selected by the maximal information coefficient algorithm, and local information is extracted. Then, observed value, accumulated error, and change rate information are further extracted from the representative process variables and expanded into multiple information blocks, which contain both local process and hidden time feature information. After that, the support vector data description model is established to monitor each information block, and the Bayesian inference is employed to fuse the final monitoring results to obtain a new monitoring index. Finally, the performance and effectiveness of the proposed method is validated by conducting a simulation on Tennessee Eastman process.

An Optimal Adaptive Variable Sample Size Scheme for the Multivariate Coefficient of Variation

Development of an efficient process monitoring system has always received great attention. Previous studies revealed that the coefficient of variation (CV) is important in ensuring process quality, especially for monitoring a process where its process mean and variance are highly correlated. The fact that almost all industrial process monitoring involves a minimum of two or more related quality characteristics being monitored simultaneously, this paper incorporates the salient feature of the adaptive sample size VSS scheme into the standard multivariate CV (MCV) chart, called the VSS MCV chart. A Markov chain model is developed for the derivation of the chart’s performance measures, i.e the average run length (ARL), the standard deviation of the run length (SDRL), the average sample size (ASS), the average number of observations to signal (ANOS) and the expected average run length (EARL). The numerical comparison shows that the proposed chart prevails over the existing standard MCV chart for detecting small and moderate upward and downward MCV shifts.

Development of Real-time Grinding Process Monitoring and Analysis System

Development of Real-time Grinding Process Monitoring and Analysis System

Architecture Design of An Intelligent Monitoring System for Turbine Filtration Device

In order to improve the operation efficiency of the turbine and reduce the failure rate of the equipment, this paper takes the inlet filter device of the turbine as the research object, comprehensively utilizes computer technology, machine learning, computer network and other technologies, and proposes a remote monitoring system of the turbine filter device based on industrial Internet cloud service and C/S architecture. The industrial process monitoring system is designed according to the actual situation of the industrial process. By connecting the system to the industrial Internet cloud service, the remote monitoring of the on-site running state and operation data of the equipment is realized. On this basis, the artificial neural network is proposed to realize the timely diagnosis and treatment of faults, and the operation and maintenance suggestions are put forward to improve the stability of equipment operation, reduce the maintenance cost of equipment, so as to improve the economic benefits of system operation and maintenance.