Difference between revisions of "VBPM"

From ISLAB/CAISR
 
Line 3: Line 3:
 
|ContactInformation=Sławomir Nowaczyk, Yuantao Fan
 
|ContactInformation=Sławomir Nowaczyk, Yuantao Fan
 
|ShortDescription=Volvo Bus Predictive Maintenance
 
|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
 
|LogotypeFile=Procedure.png
 
|ProjectResponsible=Sławomir Nowaczyk
 
|ProjectResponsible=Sławomir Nowaczyk
Line 20: Line 20:
 
== Background and Objectives ==
 
== 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.
+
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.  
 
+
 
+
This project will utilise data from Volvo bus, including Logged Vehicle Data and Vehicle Service Records.
+
  
 
== Expectations ==
 
== Expectations ==
  
 +
VBPM will drive the development of predictive maintenance innovation at Volvo Bussar AB. Proposed novel machine learning techniques for predicting component failures will be able to model component degradation and identify faults in advance, giving enough time margin for performing maintenance service and fixing worn-out components. This project will lead the transition of the current maintenance paradigm to one with more predictive maintenance at Volvo Bussar AB. The project are expect to improve maintenance efficiency and safety while reduce cost.
  
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}}
 
{{ShowProjectPublications}}

Latest revision as of 18:55, 9 November 2017



Volvo Bus Predictive Maintenance

VBPM
Project start:
January 2017
Project end:
June 2018
More info (PDF):
[[media: | pdf]]
Contact:
Sławomir Nowaczyk, Yuantao Fan
Application Area:
Intelligent Vehicles

Involved internal personnel
Involved external personnel
Involved partners

Abstract

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.

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 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

VBPM will drive the development of predictive maintenance innovation at Volvo Bussar AB. Proposed novel machine learning techniques for predicting component failures will be able to model component degradation and identify faults in advance, giving enough time margin for performing maintenance service and fixing worn-out components. This project will lead the transition of the current maintenance paradigm to one with more predictive maintenance at Volvo Bussar AB. The project are expect to improve maintenance efficiency and safety while reduce cost.