scholarly journals Oversizing Thread Diagnosis in Tapping Operation

Metals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 537
Author(s):  
Alain Gil Del Val ◽  
Fernando Veiga ◽  
Mariluz Penalva ◽  
Miguel Arizmendi
Keyword(s):  
Control Chart ◽  
Manufacturing Systems ◽  
Spindle Motor ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Knowing That ◽  
On Line ◽  
High Level ◽  
Screw Threads ◽  
Multivariate Control

Automotive, railway and aerospace sectors require a high level of quality on the thread profiles in their manufacturing systems knowing that the tapping process is a complex manufacturing process and the last operation in a manufacturing cell. Therefore, a multivariate statistical process control chart, for each tap, is presented based on the principal components of the torque signal directly measured from spindle motor drive to diagnosis the thread profile quality. This on-line multivariate control chart has implemented an alarm to avoid defected screw threads (oversized). Therefore, it could work automatically without any operator intervention assessing the thread quality and the safety is guaranteed during the tapping process.

Distribution-Free Multivariate Control Chart Based on Change-Point Model

2014 ◽  
Vol 971-973 ◽  
pp. 1435-1439
Author(s):  
Mei Hua Duan ◽  
Xue Min Zi
Keyword(s):  
Control Chart ◽  
Change Point ◽  
Point Model ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Distribution Free ◽  
Multivariate Control Chart ◽  
The Mean ◽  
Change Point Model ◽  
Multivariate Control

It is common to monitor several quality characteristics of a process simultaneously in modern quality control, and it is called multivariate statistical process control (MSPC) in the literature. A change-point control chart for detecting shifts in the mean of a multivariate statistical process is developed for the case where the nominal value of the mean is unknown but some historical samples are available. This control chart is called distribution-free multivariate control chart based on change-point model. Its distribution robustness is a significant advantage where we usually know nothing about the underlying distribution. And the simulated results show that this approach has a good performance across the range of possible shifts.

Improving Phase I Monitoring of Dirichlet Regression Profiles

2018 ◽  
Vol 25 (06) ◽  
pp. 1850030 ◽  
Author(s):  
Hourieh Foroutan ◽  
Amirhossein Amiri ◽  
Reza Kamranrad
Keyword(s):  
Phase I ◽  
Control Chart ◽  
Control Charts ◽  
Compositional Data ◽  
Ratio Test ◽  
Statistical Process ◽  
Explanatory Variables ◽  
Monitoring Procedure ◽  
Multivariate Control

In most statistical process control (SPC) applications, quality of a process or product is monitored by univariate or multivariate control charts. However, sometimes a functional relationship between a response variable and one or more explanatory variables is established and monitored over time. This relationship is called “profile” in SPC literature. In this paper, we specifically consider processes with compositional data responses, including multivariate positive observations summing to one. The relationship between compositional data responses and explanatory variables is modeled by a Dirichlet regression profile. We develop a monitoring procedure based on likelihood ratio test (lrt) for Phase I monitoring of Dirichlet regression profiles. Then, we compare the performance of the proposed method with the best method in the literature in terms of probability of signal. The results of simulation studies show that the proposed control chart has better performance in Phase I monitoring than the competing control chart. Moreover, the proposed method is able to estimate the real time of a change as well. The performance of this feature is also investigated through simulation runs which show the satisfactory performance. Finally, the application of the proposed method is illustrated based on a real case in comparison with the existing method.

Diagnosing Problems of Distribution-Free Multivariate Control Chart

2014 ◽  
Vol 971-973 ◽  
pp. 1602-1606
Author(s):  
Wen Li Shi ◽  
Xue Min Zi
Keyword(s):  
Control Chart ◽  
Historical Data ◽  
Important Application ◽  
Distribution Free ◽  
On Line ◽  
Multivariate Control Chart ◽  
Diagnostic Framework ◽  
Multivariate Control ◽  
Control Limits ◽  
New Distribution

In order to solve the problem of only have a few historical data that can be used in multivariate process monitoring, a new distribution-free multivariate control chart has been proposed. And in the control chart structure the control limits are determined on-line with the future observations and the historical data. Therefore, the proposed control chart has very important application in practice. However, the research doesn’t study the problem of the fault diagnosis after the control chart alarms. So we use LASSO-based diagnostic framework to identify when a detected shift has occurred and to isolate the shifted components.

On-Line Reconfigurable Machines

AI Magazine ◽  
2013 ◽  
Vol 34 (3) ◽  
Author(s):  
Lara S. Crawford
Keyword(s):  
Manufacturing Systems ◽  
Reconfigurable Manufacturing ◽  
Planning And Scheduling ◽  
Model Based ◽  
Planning And Control ◽  
Resource Reasoning ◽  
On Line ◽  
Scheduling Heuristic ◽  
High Level ◽  
And Control

A recent trend in intelligent machines and manufacturing has been toward reconfigurable manufacturing systems, which move away from the idea of a fixed factory line executing an unchanging set of operations, and toward the goal of an adaptable factory structure. The logical next challenge in this area is that of on-line reconfigurability. With this capability, machines can reconfigure while running, enable or disable capabilities in real time, and respond quickly to changes in the system or the environment (including faults). We propose an approach to achieving on-line reconfigurability based on a high level of system modularity supported by integrated, model-based planning and control software. Our software capitalizes on many advanced techniques from the artificial intelligence research community, particularly in model-based domain-independent planning and scheduling, heuristic search, and temporal resource reasoning. We describe the implementation of this design in a prototype highly modular, parallel printing system.

scholarly journals An Improved Dual-Kurtogram-Based T 2 Control Chart for Condition Monitoring and Compound Fault Diagnosis of Rolling Bearings

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Zhiyuan Jiao ◽  
Wei Fan ◽  
Zhenying Xu
Keyword(s):  
Fault Diagnosis ◽  
Control Chart ◽  
Condition Monitoring ◽  
Control Limit ◽  
Normal Operation ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Envelope Analysis ◽  
Spectral Kurtosis ◽  
Vibration Data

Condition monitoring and compound fault diagnosis are crucial key points for ensuring the normal operation of rotating machinery. A novel method for condition monitoring and compound fault diagnosis based on the dual-kurtogram algorithm and multivariate statistical process control is established in this study. The core idea of this method is the capability of the dual-kurtogram in extracting subbands. Vibration data under normal conditions are decomposed by the dual-kurtogram into two subbands. Then, the spectral kurtosis (SK) of Subband I and the envelope spectral kurtosis (ESK) of Subband II are formulated to construct a control limit based on kernel density estimation. Similarly, vibration data that need to be monitored are constructed into two subbands by the dual-kurtogram. The SK of Subband I and the ESK of Subband II are calculated to derive T 2 statistics based on the covariance determinant. An alarm will be triggered when the T 2 statistics exceed the control limit and suitable subbands for square envelope analysis are adopted to obtain the characteristic frequency. Simulation and experimental data are used to verify the feasibility of the proposed method. Results confirm that the proposed method can effectively perform condition monitoring and fault diagnosis. Furthermore, comparison studies show that the proposed method outperforms the traditional T 2 control chart, envelope analysis, and empirical mode decomposition.

Monitoring of overall equipment effectiveness by multivariate statistical process control

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Imane Mjimer ◽  
ES-Saadia Aoula ◽  
EL Hassan Achouyab
Keyword(s):  
Control Chart ◽  
Customer Demand ◽  
Statistical Process ◽  
Quality Performance ◽  
Content Type ◽  
Multivariate Control Chart ◽  
Performance Rate ◽  
Multivariate Control ◽  
Six Sigma Approach ◽  
Control Limits

Purpose This study aims to monitor the overall equipment effectiveness (OEE) indicator that is one of the best indicators used to monitor the performance of the company by the multivariate control chart. Design/methodology/approach To improve continually the performance of a company, many research studies tend to apply Lean six sigma approach. It is one of the best ways used to reduce the variability in the process by using the univariate control chart to know the trend of the variable and make the action before process deviation. Nevertheless, and when the need is to monitor two or more correlated characteristics simultaneously, the univariate control chart will be unable to do it, and the multivariate control chart will be the best way to successfully monitor the correlated characteristics. Findings For this study, the authors have applied the multivariate control chart to control the OEE performance rate which is composed by the quality rate, performance rate and availability rate, and the relative work from which the authors have adopted the same methodology (Hadian and Rahimifard, 2019) was done for project monitoring, which is done by following different indicators such as cost, and time; the results of this work shows that by applying this tool, all project staff can meet the project timing with the cost already defined at the beginning of the project. The idea of monitoring the OEE rate comes because the OEE contains the three correlated indicators, we can’t do the monitoring of the OEE just by following one of the three because data change and if today we have the performance and quality rate are stable, and the availability is not, tomorrow we can another indicator impacted and, in this case, the univariate control chart can’t response to our demand. That’s why we have choose the multivariate control chart to prevent the trend of OEE performance rate. Otherwise, and according to production planning work, they try to prevent the downtime by switching to other references, but after applying the OEE monitoring using the multivariate control chart, the company can do the monitoring of his ability to deliver the good product at time to meet customer demand. Research limitations/implications The application was done per day, it will be good to apply it per shift in order to have the ability to take the fast reaction in case of process deviation. The other perspective point we can have is to supervise the process according to the control limits found and see if the process still under control after the implementation of the Multivariate control chart at the OEE Rate and if we still be able to meet customer demand in terms of Quantity and Quality of the product by preventing the process deviation using multivariate control chart. Practical implications The implication of this work is to provide to the managers the trend of the performance of the workshop by measuring the OEE rate and by following if the process still under control limits, if not the reaction plan shall be established before the process become out of control. Originality/value The OEE indicator is one of the effective indicators used to monitor the ability of the company to produce good final product, and the monitoring of this indicator will give the company a visibility of the trend of performance. For this reason, the authors have applied the multivariate control chart to supervise the company performance. This indicator is composed by three different rates: quality, performance and availability rate, and because this tree rates are correlated, the authors have tried to search the best tool which will give them the possibility to monitor the OEE rate. After literature review, the authors found that many works have used the multivariate control chart, especially in the field of project: to monitor the time and cost simultaneously. After that, the authors have applied the same approach to monitor the OEE rate which has the same objective : to monitor the quality, performance and availability rate in the same time.

MONITORING OF BATCH PROCESSES WITH VARYING DURATIONS BASED ON THE HAUSDORFF DISTANCE

2006 ◽  
Vol 13 (03) ◽  
pp. 213-236 ◽  
Author(s):  
PHILIPPE CASTAGLIOLA ◽  
ARIANE FERREIRA PORTO ROSA
Keyword(s):  
Hausdorff Distance ◽  
Batch Process ◽  
Batch Processes ◽  
Multivariate Statistical Process Control ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Innovative Methods ◽  
Batch Process Monitoring ◽  
On Line ◽  
Case Data

In some industrial situations, the classical assumption used in the batch process monitoring that all batches have equal durations and are synchronized does not hold. A batch process is carried out in sequential phases and a significant variability generally occurs in the duration of the phases such that events signifying the beginning or the end of a phase are generally misaligned in time within the various batches. The consequence is that the variable trajectories, in the different runs of the same batch process, are unsynchronized. In this case, data analysis from process for performing the multivariate statistical process control can be difficult. In this paper, we propose several innovative methods for the off-line and on-line monitoring of batch processes with varying durations, all based on the Hausdorff distance. These methods have been successfully tested on a simulated example and on an industrial case example. The conclusion is that these methods are able to efficiently discriminate between nominal and non-nominal batches.

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