Condition-Based Maintenance Optimization for Motorized Spindles Integrating Proportional Hazard Model with SPC Charts

Du, Xuejiao; Gai, Jingbo; Chen, Cen

doi:10.1155/2020/7618376

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…[47] This paper explores the integration of PM techniques and SPC tools in designing control charts and production/maintenance policies for unreliable manufacturing systems, enhancing reliability and quality. [48] This paper proposes a condition-based maintenance optimization method for motorized spindles integrating a proportional hazard model with SPC charts, demonstrating how combining PM techniques with SPC can enhance maintenance scheduling and reliability in industrial settings. [49] This paper uses predictive process control (PPC) with data mining techniques to stabilize processes, enhance quality, and reduce variability.…”

Section: Table 1 Studies Involving the Integration Of Spc And Pmmentioning

confidence: 99%

Empowering Manufacturing Environments with Process Mining-Based Statistical Process Control

Dogan,

Areta Hiziroglu

2024

Machines

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The production of high-quality products and efficient manufacturing processes in modern environments, where processes vary widely, is one of the most crucial issues today. Statistical process control (SPC) and process mining (PM) effectively trace and enhance the manufacturing processes. In this direction, this paper proposes an innovative approach involving SPC and PM strategies to empower the manufacturing environment. SPC monitors key performance indicators (KPIs) and identifies out-of-control processes that deviate from specification limits, while PM discovery techniques are applied for those abnormal processes to extract the actual process flow from event logs and model it using Petri nets. Different enhancement techniques in PM, such as decision rules and root cause analysis, are then used to return the process to control and prevent future deviations. The application of the integrated SPC–PM approach is shown through case studies of production processes. SPC charts found that over 6% of processes exceeded specification limits. At the same time, PM methodologies revealed that prolonged times for the ‘Quality Control’ activity is the fundamental factor increasing the cycle time. Moreover, decision tree analysis provides rules for decreasing the cycle times of unbalanced processes. The absence of a transition from the ‘Return from Waiting’ activity to ‘Packing and Shipment’ is a critical factor in decreasing cycle times, as is the shift information. Our newly proposed methodology, which combines process analysis from PM with statistical monitoring from SPC, ensures operational excellence and consistent quality in manufacturing. This study illustrates the application of the proposed methodology through a case study in production processes, highlighting its effectiveness in identifying and addressing process deviations.

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Section: Table 1 Studies Involving the Integration Of Spc And Pmmentioning

confidence: 99%

Empowering Manufacturing Environments with Process Mining-Based Statistical Process Control

Dogan,

Areta Hiziroglu

2024

Machines

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“…Statistical process control (SPC) technology refers to the analysis and real-time monitoring of products based on statistical analysis technology and control chart, early warning of abnormal trends in the production process, and scientifically distinguishing the change trend of real-time operation parameters [19]. In recent years, with the continuous rise of intersection, integration, and penetration between different disciplines [20], SPC technology has been gradually applied to fault diagnosis in the oil and gas industry.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method and Application of ESP Well Based on SPC Rules and Real-Time Data Fusion

Yang

Wang

Zheng³

et al. 2022

Mathematical Problems in Engineering

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Aiming at the popularization and application of a real-time monitoring parameter acquisition system of the electric submersible pump (ESP) well, this paper proposes a fault diagnosis method of ESP well operation based on the SPC rule and prior knowledge fusion. Based on the study of parameter variation rules of ESP well, the SPC expansion rule model is established; by analyzing the variation of some typical characteristic parameters of ESP well, combined with SPC expansion rules and expert experience, a priori knowledge of fault diagnosis of ESP well is formed, that is, multiparameter fault analysis table and weight factor; the SPC extended rule model and prior knowledge are fused to establish the fault probability model of ESP well, form the fault diagnosis method of ESP well, develop the online fault diagnosis software of ESP well, and deploy it in 425 ESP wells in a block. Taking five types of tubing leakage, pump wear, shaft breakage, gas influence, and pump plugging as examples, the application process of fault diagnosis method is analyzed. The research and application show that compared with other fault diagnosis methods, this method needs a smaller time window and higher diagnosis accuracy. By setting multiple time windows, this diagnosis method is applied to calculate the fault probability of ESP well in real time, solve the real time and accurate identification of 14 sudden faults and gradual faults, and significantly improve the intelligent diagnosis level of production faults of ESP well.

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Operations-aware novelty detection framework for CNC machine tools: proposal and application

Macchi,

Cattaneo,

Polenghi

et al. 2023

Int J Adv Manuf Technol

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Digitisation offers manufacturing companies new opportunities to improve their operations and competitiveness in the market by unleashing potentialities related to real-time monitoring and control of operating machines. Through condition-based and predictive maintenance, the knowledge about the health state and probability of failure of the machines is improved for better decision-making. Amongst them, CNC machine tools do represent a complex case from a maintenance viewpoint as their operations are ever-changing and their high reliability brings to a lack, or limited set, of run-to-failure data. To address the problem, the research work proposes an operations-aware novelty detection framework for CNC machine tools based on already-in-place controllers. The framework is based on statistical modelling of the behaviour of the machine tools, namely through gradient boosting regression and Gaussian mixture models, to identify the health state considering varying operations through time. The proposed solution is verified on sixteen multi-axis CNC machine tools in a large manufacturing company. The results show that the proposed solution can effectively support maintenance decisions by informing on the health states while discerning between varying operations and abnormal/faulty states of interest. This solution represents a brick in a cloud-edge-based industrial information system stack that can be further developed for shop floor-integrated decision-making.

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Condition-Based Maintenance Optimization for Motorized Spindles Integrating Proportional Hazard Model with SPC Charts

Cited by 4 publications

References 27 publications

Empowering Manufacturing Environments with Process Mining-Based Statistical Process Control

Empowering Manufacturing Environments with Process Mining-Based Statistical Process Control

Fault Diagnosis Method and Application of ESP Well Based on SPC Rules and Real-Time Data Fusion

Operations-aware novelty detection framework for CNC machine tools: proposal and application

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