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Article information
2025 , Volume 30, ¹ 1, p.15-23
Zakrevskaya N.S., Kovalevskii A.P.
Detecting the lack of functioning of elevators using the change point detection method
We study the emergence of a linear Poisson regression model from the problem of statistical analysis of a time-inhomogeneous Poisson process and its application to elevator performance analysis. Methodology. We use a linear Poisson regression model, which assumes that the responses are independent and each is Poisson distributed with a parameter equal to the linear combination of the regressors. Findings. We studied the regression model that arises when observing a time-inhomogeneous Poisson process. We have shown that if the regressor matrix has full rank, then the maximum likelihood estimates have a simple explicit form. In this case, they do not involve a specific type of regression function and coincide with a known parameter estimate based on a sample from Poisson distribution. The elevator reliability index is calculated as the ratio of the number of successful activations of the elevator main drive multiplied by 100 to the estimate of the number of all activations requested by users. We carried out parameter estimation and stochastic simulation for the elevator operation. Based on the results of the analysis, it is recommended to calculate the elevator reliability index using data on weekdays with the exception of pre-weekends and holidays. Originality/value. The paper derives a linear Poisson regression model for the number of calls in fixed time intervals using assumptions of a linear regression model for the intensity of the Poisson flow. The case of periodic intensity is studied and conditions are found under which the estimates of the regression parameters have a simple explicit form. The developed evaluation algorithm is applied to determining periods of elevator lack of functioning and calculating the elevator reliability index. The calculation results were verified by direct stochastic modelling.
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Keywords: Poisson process, change point, regression, elevator
doi: 10.25743/ICT.2025.30.1.003
Author(s):
Zakrevskaya Natalia Stanislavovna
Position: Senior Fellow
Office: Novosibirsk State Technical University
Address: 630073, Russia, Novosibirsk, 20, prospekt K. Marksa
Phone Office: (383) 3463226
E-mail: natali.erlagol@gmail.com
Kovalevskii Artem Pavlovich
Dr. , Associate Professor
Position: Leading research officer
Office: Novosibirsk State Technical University
Address: 630073, Russia, Novosibirsk, 20, prospekt K. Marksa
Phone Office: (383) 3463226
E-mail: artyom.kovalevskii@gmail.com
SPIN-code: 5702-8998 References:
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Bibliography link:
Zakrevskaya N.S., Kovalevskii A.P. Detecting the lack of functioning of elevators using the change point detection method // Computational technologies. 2025. V. 30. ¹ 1. P. 15-23
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