Anomaly Detection for Heavy-duty Vehicles - Revision history

Islab at 14:48, 10 November 2025

2025-11-10T14:48:16Z

Islab at 13:36, 10 November 2025

2025-11-10T13:36:14Z

Islab at 13:35, 10 November 2025

2025-11-10T13:35:25Z

Islab at 23:10, 28 October 2025

2025-10-28T23:10:47Z

Islab at 23:08, 28 October 2025

2025-10-28T23:08:46Z

Islab at 23:06, 28 October 2025

2025-10-28T23:06:58Z

Islab at 14:51, 28 October 2025

2025-10-28T14:51:50Z

Islab at 14:39, 28 October 2025

2025-10-28T14:39:33Z

Islab at 14:39, 28 October 2025

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Islab at 12:56, 28 October 2025

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← Older revision		Revision as of 14:48, 10 November 2025
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	Please apply via the following link (in Volvo portal):		Please apply via the following link (in Volvo portal):

−	https://jobs.volvogroup.com/job~~/G%C3%B6teborg-Master-Thesis-Anomaly-detection-in-time-series-for-heavy-duty-BEVs-417~~-15/~~1330250455~~/	+	https://jobs.volvogroup.com/job-invite/26403/

@@ Line 27: / Line 27: @@
 Please contact Yuantao for more details.
-Please apply via the following link:
+Please apply via the following link (in Volvo portal):
 https://jobs.volvogroup.com/job/G%C3%B6teborg-Master-Thesis-Anomaly-detection-in-time-series-for-heavy-duty-BEVs-417-15/1330250455/

← Older revision		Revision as of 13:35, 10 November 2025
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	Please contact Yuantao for more details.		Please contact Yuantao for more details.
		+
		+	Please apply via the following link:
		+
		+	https://jobs.volvogroup.com/job/G%C3%B6teborg-Master-Thesis-Anomaly-detection-in-time-series-for-heavy-duty-BEVs-417-15/1330250455/

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 By applying anomaly detection techniques to identify early signs of component degradation or failure, this project seeks to enable proactive and predictive maintenance. Such a system would minimize unplanned downtime, extend component lifespan, and reduce overall maintenance costs, thereby enhancing the safety, reliability, and economic performance of heavy-duty vehicle fleets. A promising research direction involves investigating representation learning techniques (such as contrastive learning, continual learning, federated learning etc.) to capture and encode key characteristics of time series data for anomaly detection. For instance, many state-of-the-art time methods utilize autoencoders, using learned embeddings in the latent features or reconstruction errors to compute anomaly scores. In addition, methods that are inherently explainable (e.g. causal graph/relations learned via causal inferences), that can be learned in an incremental setting (e.g. decision tree-based approaches) or computationally efficient (e.g. echo state network via reservoir computing), are of great interest as well. The developed approach will be evaluated on a real-world dataset collected from commercial vehicle fleets.
-You will work with the Advanced Analytics Team (Volvo Group Technology) and collaborate with domain experts, stakeholders in different tech streams.
+You will work with the Advanced Analytics Team (Volvo Group) and collaborate with domain experts, stakeholders in different tech streams.
 Please contact Yuantao for more details.

@@ Line 23: / Line 23: @@
 By applying anomaly detection techniques to identify early signs of component degradation or failure, this project seeks to enable proactive and predictive maintenance. Such a system would minimize unplanned downtime, extend component lifespan, and reduce overall maintenance costs, thereby enhancing the safety, reliability, and economic performance of heavy-duty vehicle fleets. A promising research direction involves investigating representation learning techniques (such as contrastive learning, continual learning, federated learning etc.) to capture and encode key characteristics of time series data for anomaly detection. For instance, many state-of-the-art time methods utilize autoencoders, using learned embeddings in the latent features or reconstruction errors to compute anomaly scores. In addition, methods that are inherently explainable (e.g. causal graph/relations learned via causal inferences), that can be learned in an incremental setting (e.g. decision tree-based approaches) or computationally efficient (e.g. echo state network via reservoir computing), are of great interest as well. The developed approach will be evaluated on a real-world dataset collected from commercial vehicle fleets.
-You will work with the Advanced Analytics Team, at Volvo Group Technology, and collaborate with domain experts, stakeholders in different tech streams.
+You will work with the Advanced Analytics Team (Volvo Group Technology) and collaborate with domain experts, stakeholders in different tech streams.
 Please contact Yuantao for more details.

@@ Line 1: / Line 1: @@
 {{StudentProjectTemplate
-|Summary=develop context-aware and explainable anomaly detection algorithms for monitoring critical components and their efficiencies in heavy-duty vehicles; in collaboration with Volvo Group
+|Summary=develop contextual and explainable anomaly detection algorithms for monitoring critical components and their efficiencies in heavy-duty vehicles; in collaboration with Volvo Group
 |TimeFrame=Fall 2025 or Spring 2026
 |References=Li, Z., Zhu, Y., & Van Leeuwen, M. (2023). A survey on explainable anomaly detection. ACM Transactions on Knowledge Discovery from Data, 18(1), 1-54.
@@ Line 19: / Line 19: @@
 |Status=Open
 }}
-Ensuring operational safety is crucial for commercial heavy-duty vehicles. The aim of this project is to design and develop context-aware and explainable anomaly detection algorithms for monitoring critical components and their efficiencies in heavy-duty vehicles. Beyond safety considerations, cost-efficient and reliable maintenance strategies are essential for operating commercial vehicle fleets, where operational availability and total maintenance cost directly impact profitability.
+Ensuring operational safety is crucial for commercial heavy-duty vehicles. The aim of this project is to design and develop contextual and explainable anomaly detection algorithms for monitoring critical components and their efficiencies in heavy-duty vehicles. Beyond safety considerations, cost-efficient and reliable maintenance strategies are essential for operating commercial vehicle fleets, where operational availability and total maintenance cost directly impact profitability.
 By applying anomaly detection techniques to identify early signs of component degradation or failure, this project seeks to enable proactive and predictive maintenance. Such a system would minimize unplanned downtime, extend component lifespan, and reduce overall maintenance costs, thereby enhancing the safety, reliability, and economic performance of heavy-duty vehicle fleets. A promising research direction involves investigating representation learning techniques (such as contrastive learning, continual learning, federated learning etc.) to capture and encode key characteristics of time series data for anomaly detection. For instance, many state-of-the-art time methods utilize autoencoders, using learned embeddings in the latent features or reconstruction errors to compute anomaly scores. In addition, methods that are inherently explainable (e.g. causal graph/relations learned via causal inferences), that can be learned in an incremental setting (e.g. decision tree-based approaches) or computationally efficient (e.g. echo state network via reservoir computing), are of great interest as well. The developed approach will be evaluated on a real-world dataset collected from commercial vehicle fleets.