VBPM - Revision history

Yuantao at 17:55, 9 November 2017

2017-11-09T17:55:13Z

Yuantao at 17:22, 9 November 2017

2017-11-09T17:22:17Z

Yuantao at 15:40, 9 November 2017

2017-11-09T15:40:20Z

Yuantao: Created page with "{{ResearchProjInfo |Title= VBPM |ContactInformation=Sławomir Nowaczyk, Yuantao Fan |ShortDescription=Volvo Bus Predictive Maintenance |LogotypeFile=Procedure.png |ProjectResp..."

2017-11-09T15:38:39Z

Created page with "{{ResearchProjInfo |Title= VBPM |ContactInformation=Sławomir Nowaczyk, Yuantao Fan |ShortDescription=Volvo Bus Predictive Maintenance |LogotypeFile=Procedure.png |ProjectResp..."

New page

{{ResearchProjInfo
|Title= VBPM
|ContactInformation=Sławomir Nowaczyk, Yuantao Fan
|ShortDescription=Volvo Bus Predictive Maintenance
|LogotypeFile=Procedure.png
|ProjectResponsible=Sławomir Nowaczyk
|ProjectStart=2017/01
|ProjectEnd=2018/06
|ApplicationArea=Intelligent Vehicles
|Lctitle=No
}}
{{AssignProjPartner
|projectpartner=Region Halland
}}
__NOTOC__
{{ShowResearchProject}}

== Purpose and goals ==
Unplanned downtime can be avoided by accurate prediction of the failure through continuously monitoring of vehicles’ health status. However, to reveal patterns behind failures in a system as complex as a modern truck, new methods for analysing the data need to be developed. The HEALTH project aims to create a sequence model capturing the lifelong history of a truck, and use it to explain relations between different events such as failures, repairs, fault codes - leading to better maintenance.

== Expected effects and results ==
HEALTH will enhance current 100% uptime promise of Volvo Trucks by expanding the existing range of predictive maintenance solutions. Novel Machine Learning methods for representing lifelong histories of trucks will be used to precisely identify vehicles that are likely to fail soon, and corrective actions will be suggested based on the probable failure causes. Overall effects will include prolonging vehicle life, providing more timely and cheaper maintenance, and increasing traffic safety.

== Scheduled planning and implementation ==
The HEALTH project is planned for two years, starting October 2017. The work will be carried out in a close collaboration between Volvo Trucks Aftermarket and Halmstad University. The project is divided into five work packages focusing on data aggregation, fully and partially observable sequence modeling, causal analysis and the demonstrator. Implementation includes research and development of new machine learning methods, their deployment, and finally evaluation in real business setting.

{{ShowProjectPublications}}

@@ Line 3: / Line 3: @@
 |ContactInformation=Sławomir Nowaczyk, Yuantao Fan
 |ShortDescription=Volvo Bus Predictive Maintenance
-|Description=The overall objective of this project is to improve uptime for Volvo buses as well as scheduling maintenance cost-effectively. Guaranteeing vehicle uptime is important since downtime due to component failures are increasingly difficult to dealt with as the complexity of modern transport solution increases. The project is aiming at developing a framework, powered by machine learning technique, for predicting component failures in buses and providing fleet operator decision support for scheduling maintenance. Proposed machine learning models will be built, tested and validated based on real data. This project is a collaboration with Volvo Bussar AB and Volvo Truck Technology.
+|Description=The overall objective of this project is to improve uptime for Volvo buses as well as scheduling maintenance cost-effectively. Guaranteeing vehicle uptime is important since downtime caused by component failures are increasingly difficult to identify and being dealt with as the complexity of modern transport solution increases. The project is aiming at developing a framework, powered by machine learning technique, for predicting component failures in buses and providing fleet operator decision support for scheduling maintenance. Proposed machine learning models will be built, tested and validated based on real data. This project is a collaboration with Volvo Bussar AB and Volvo Truck Technology.
 |LogotypeFile=Procedure.png
 |ProjectResponsible=Sławomir Nowaczyk
@@ Line 20: / Line 20: @@
 == Background and Objectives ==
 The current paradigm for maintaining industrial equipments is a combination of reactive and preventive actions. Take commercial transportation vehicles as example, they are typically maintained after an equipment failure occurs or according to preplanned visits to the workshops based on mileage or calendar time. This mixture of maintenance strategy is not ideal: i ) it does not perform maintenance pro-actively well before the failure happens, i.e. severe component failures usually result in extra damage to the system and could be prevented; ii ) planned maintenance with fixed time intervals does not guarantee all routinely changed parts have used all their potentials. Therefore, a shift of current maintenance strategy to one with more predictive maintenance is required: to inspect and repair components (well) before they causes a breakdown or severe damage to the system.
-Nowadays, with the development of electronic devices and the emergence of Internet of Things, huge amount of sensor data collected and transmitted remotely can be utilized for equipment monitoring, fault detection and prognostics. By processing sensor date during operations, condition of the equipment will be accessed and maintenance decision will be made. This project will improve the work carried out in
+Nowadays, with the development of electronic devices and the emergence of Internet of Things, huge amount of sensor data collected and transmitted remotely can be utilized for equipment monitoring, fault detection and prognostics. By processing sensor date during operations, condition of the equipment will be accessed and maintenance decision will be made. This project will improve the work carried out in VPMS project. The proposed framework is capable of extracting relevant data sources from LVD (Logged Vehicle Data) and VSR (Vehicle Services Records) for predicting component failure uses, finding useful features and generate prediction models for component of importance and interests.
 == Expectations ==
 {{ShowProjectPublications}}

@@ Line 1: / Line 1: @@
 {{ResearchProjInfo
-|Title= VBPM
+|Title=VBPM
 |ContactInformation=Sławomir Nowaczyk, Yuantao Fan
 |ShortDescription=Volvo Bus Predictive Maintenance
-|Description=The aim of Active@Work is to explore if mobile technology including a personalized decision support system, can have any effect on physical activity level, health, work ability, quality of life, work productivity or sick leave among individuals with osteoarthritis (OA). We also aim to study if there is any difference in effect between using mobile technology and activity monitoring alone or when continuous feedback concerning physical activity is added.
+|Description=The overall objective of this project is to improve uptime for Volvo buses as well as scheduling maintenance cost-effectively. Guaranteeing vehicle uptime is important since downtime due to component failures are increasingly difficult to dealt with as the complexity of modern transport solution increases. The project is aiming at developing a framework, powered by machine learning technique, for predicting component failures in buses and providing fleet operator decision support for scheduling maintenance. Proposed machine learning models will be built, tested and validated based on real data. This project is a collaboration with Volvo Bussar AB and Volvo Truck Technology.
 |LogotypeFile=Procedure.png
 |ProjectResponsible=Sławomir Nowaczyk
@@ Line 12: / Line 11: @@
 |Lctitle=No
 }}
 {{AssignProjPartner
-|projectpartner=Region Halland
+|projectpartner=Volvo Bussar AB
 }}
 __NOTOC__
 {{ShowResearchProject}}
-== Purpose and goals ==
+== Background and Objectives ==
-Unplanned downtime can be avoided by accurate prediction of the failure through continuously monitoring of vehicles’ health status. However, to reveal patterns behind failures in a system as complex as a modern truck, new methods for analysing the data need to be developed. The HEALTH project aims to create a sequence model capturing the lifelong history of a truck, and use it to explain relations between different events such as failures, repairs, fault codes - leading to better maintenance.
 HEALTH will enhance current 100% uptime promise of Volvo Trucks by expanding the existing range of predictive maintenance solutions. Novel Machine Learning methods for representing lifelong histories of trucks will be used to precisely identify vehicles that are likely to fail soon, and corrective actions will be suggested based on the probable failure causes. Overall effects will include prolonging vehicle life, providing more timely and cheaper maintenance, and increasing traffic safety.