THESIS: Evaluating Predictive Maintenance Models for Industrial Device-to-Cloud Architectures
2 days ago
This thesis focuses on evaluating different predictive maintenance models using provided industrial datasets. The goal is to determine which models are most effective in predicting failures and enabling proactive maintenance. The study will compare approaches such as anomaly detection, time-series forecasting, and classification-based models, with the evaluation based on accuracy, efficiency, and suitability for real-time applications.
Who are we looking for?Bachelor/Master of Science in Computer Science, Computer Engineering, or related fields.
Project descriptionPredictive maintenance is an essential strategy in modern industries, enabling the detection of equipment issues before they lead to costly failures or downtime. While industrial devices generate large volumes of operational data, there is uncertainty about which predictive maintenance models are most effective when applied to real world datasets.
This project will focus exclusively on evaluating and comparing predictive maintenance models using raw industrial data provided for the study. Models such as statistical anomaly detection, machine learning classification, and time series forecasting will be implemented and tested. The evaluation will measure each model's predictive accuracy, computational efficiency, and ability to handle streaming or large-scale data. The outcomes will highlight the strengths and limitations of different approaches and provide guidance for choosing suitable models for industrial predictive maintenance.
Purpose and ScopeThe purpose of this thesis is to evaluate existing predictive maintenance models and compare their effectiveness in handling industrial data. The project aims to provide a structured comparison of model performance to support industries in selecting the most suitable approaches for predictive maintenance applications.
Research Question (example)
Which predictive maintenance models are most effective for analyzing industrial datasets, and how do they compare in terms of prediction accuracy, computational efficiency, and scalability for real-time applications?
An Exciting Journey with Knightec Group
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Practical Information
This is a thesis position, located at our office in Sundsvall. Start date January or March 2026.
Please submit your application as soon as possible, but no later than If you have any questions, you are welcome to contact Johanna Edström. Note that due to GDPR, we only accept applications through our careers page.
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