Modeling and Predicting the Lifespan of Household Appliances: A Simulation-Based Approach considering Smart Usage, Maintenance Strategies and Economic Impact

Abstract

The increasing focus on sustainability and resource efficiency highlights the need to
understand not only the energy performance but also the lifetime behavior of household appliances. While modern devices have become more energy efficient, their overall
environmental impact remains strongly influenced by durability and replacement frequency. However, existing models often treat degradation, usage behavior, and smart
operational control as separate domains, limiting their explanatory power for real-world
conditions.
The objective of this thesis is to develop an integrated, simulation-based framework capable of quantifying how user behavior, environmental stress, preventive maintenance, and smart functions influence the physical lifetime and economic performance
of household appliances. To achieve this, a modular Monte Carlo approach was implemented in Python, combining stochastic sampling of user and environmental inputs with
component-level Weibull degradation models. The framework integrates a proactive replacement strategy that renews components based on statistical failure probabilities and
an economic module that compares repair and replacement in terms of annualized cost
efficiency.
The model was applied to the following three representative appliances,refrigerator,
washing machine, and electric vehicle wallbox, and validated against empirical lifetime
ranges from literature and manufacturer data. The results show that usage intensity and
thermal stress are the dominant drivers of degradation. Smart functions, modeled as
adaptive control mechanisms, reduced operational stress and extended median lifetimes
by up to 10%, while proactive replacement strategies nearly doubled the effective service
life without increasing annualized costs.
The findings demonstrate that appliance lifetime emerges from the interaction between human behavior, technical design, and environmental conditions. By linking probabilistic degradation modeling with behavioral and economic factors, this work provides
a reproducible methodology for assessing lifetime extension strategies and offers a scientific basis for future research on durability optimization and circular economy applications.

Date of Award	2025
Original language	English (American)
Supervisor	Andreas Müller (Supervisor)