scholarly journals Combined principal component analysis and proportional hazard model for optimizing condition-based maintenance

2021 ◽  
Vol 1201 (1) ◽  
pp. 012088
Author(s):  
T Bankole-Oye ◽  
I El-Thalji ◽  
J Zec
Keyword(s):  
Principal Component Analysis ◽  
Big Data ◽  
Complex Systems ◽  
Condition Monitoring ◽  
Principal Component ◽  
High Volume ◽  
Hazard Model ◽  
Monitoring Data ◽  
Proportional Hazard ◽  
Proportional Hazard Model

Abstract Large companies are investing heavily in digitalization to be more competitive and economically viable. Hence, physical assets and maintenance operations have been digitally transformed to transmit a high volume of data, e.g., condition monitoring data. Such high-volume data can be useful to optimize maintenance operations and minimize maintenance and replacement costs. A tool to optimize maintenance using condition monitoring data is the Proportional hazard model (PHM). However, it is challenging to implement PHM for industrial complex systems that generate big data. Therefore, machine learning algorithms shall support PHM method to handle such a high volume of data. Thus, the purpose of this paper is to explore how to support PHM with Principal Component Analysis (PCA) to maintenance optimization of complex industrial systems. A case study of hydraulic power unit was purposefully selected to apply and validate the proposed analytical approach. The results show that PCA supported PHM optimizes and extends the preventive maintenance interval by 79.27% which might lead to maintenance cost reductions. This model enables PHM to handle complex systems where big data is collected.

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

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xuejiao Du ◽  
Jingbo Gai ◽  
Cen Chen
Keyword(s):  
Condition Monitoring ◽  
Hazard Model ◽  
Numerical Control ◽  
Condition Based Maintenance ◽  
Cnc Machine Tools ◽  
Maintenance Cost ◽  
Proportional Hazard ◽  
Cnc Machine ◽  
Proportional Hazard Model ◽  
Motorized Spindles

Reliability of motorized spindles has a great effect on the performance and productivity of computer numerical control (CNC) machine tools for intelligent manufacturing. Condition-based maintenance (CBM) is an efficient method to prevent serious failures, to improve system reliability, and to reduce management costs for motorized spindles. However, owing to various degradation features acquired during condition monitoring, the challenge is to propose an appropriate feature to evaluate the reliability level of motorized spindles and to set up optimal CBM policies. Based on the motivation, a three-stage approach is proposed in this paper. In the first stage, proportional hazard model (PHM) is developed to describe the reliability considering failure events together with multiple degradation features. Next, statistical process control (SPC) charts are constructed for condition monitoring and anomaly detection in order to achieve early detection of potential failures. At last, a CBM schedule is modeled in consideration of maintenance cost minimization; the maintenance plan is optimized by determining the optimal control limits of SPC charts.

scholarly journals A Study of Factors Affecting the Length of Hospital Stay of COVID-19 Patients by Cox-Proportional Hazard Model in a South Indian Tertiary Care Hospital

2021 ◽  
Vol 12 ◽  
pp. 215013272110002
Author(s):  
Gayathri Thiruvengadam ◽  
Marappa Lakshmi ◽  
Ravanan Ramanujam
Keyword(s):  
Hospital Stay ◽  
Tertiary Care ◽  
Length Of Hospital Stay ◽  
Hazard Model ◽  
Cox Proportional Hazard Model ◽  
Care Hospital ◽  
Proportional Hazard ◽  
Proportional Hazard Model ◽  
Cox Proportional Hazard ◽  
South Indian

Background: The objective of the study was to identify the factors that alter the length of hospital stay of COVID-19 patients so we have an estimate of the duration of hospitalization of patients. To achieve this, we used a time to event analysis to arrive at factors that could alter the length of hospital stay, aiding in planning additional beds for any future rise in cases. Methods: Information about COVID-19 patients was collected between June and August 2020. The response variable was the time from admission to discharge of patients. Cox proportional hazard model was used to identify the factors that were associated with the length of hospital stay. Results: A total of 730 COVID-19 patients were included, of which 675 (92.5%) recovered and 55 (7.5%) were considered to be right-censored, that is, the patient died or was discharged against medical advice. The median length of hospital stay of COVID-19 patients who were hospitalized was found to be 7 days by the Kaplan Meier curve. The covariates that prolonged the length of hospital stay were found to be abnormalities in oxygen saturation (HR = 0.446, P < .001), neutrophil-lymphocyte ratio (HR = 0.742, P = .003), levels of D-dimer (HR = 0.60, P = .002), lactate dehydrogenase (HR = 0.717, P = .002), and ferritin (HR = 0.763, P = .037). Also, patients who had more than 2 chronic diseases had a significantly longer length of stay (HR = 0.586, P = .008) compared to those with no comorbidities. Conclusion: Factors that are associated with prolonged length of hospital stay of patients need to be considered in planning bed strength on a contingency basis.

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