Consensus self-organized models for fault detection (COSMO)

Byttner, Stefan; Rögnvaldsson, Thorsteinn; Svensson, Magnus

doi:10.1016/j.engappai.2011.03.002

Cited by 42 publications

(34 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then with development in manufacturing of vehicle, trend moved from diagnostics of vehicles to prognostic using sensor data as large number of sensors were being implemented in vehicles. Using these sensor's data, a new model for fault prediction "Consensus Self-Organized Model for Fault Prediction" was presented Byttner et al [6] in which model learned interesting relationships between sensor's data in vehicles and also in small mechatronic systems. Prytz et al [12] presented an unsupervised method to find relations between sensor's data in two rounds; in first round, good relations were found by MSE (Mean Square Error); then in second round LASSO (Least Absolute Shrinkage and Selection Operator) and least square error were used to determine model parameters.…”

Section: Related Workmentioning

confidence: 99%

“…A detail discussion of predictive maintenance of vehicle industry is presented by Prytz [1] in which comprehensive analysis of how machine learning approaches are being used in automotive industry for fault prediction is also discussed. "Consensus self-organized models for fault detection (COSMO)" was presented [6] in which sensor's data is used. Heavy trucks and city bus were used in experiments.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Vehicle Remote Health Monitoring and Prognostic Maintenance System

Shafi

Safi

Raza

et al. 2018

Journal of Advanced Transportation

View full text Add to dashboard Cite

In many industries inclusive of automotive vehicle industry, predictive maintenance has become more important. It is hard to diagnose failure in advance in the vehicle industry because of the limited availability of sensors and some of the designing exertions. However with the great development in automotive industry, it looks feasible today to analyze sensor's data along with machine learning techniques for failure prediction. In this article, an approach is presented for fault prediction of four main subsystems of vehicle, fuel system, ignition system, exhaust system, and cooling system. Sensor is collected when vehicle is on the move, both in faulty condition (when any failure in specific system has occurred) and in normal condition. The data is transmitted to the server which analyzes the data. Interesting patterns are learned using four classifiers, Decision Tree, Support Vector Machine, Nearest Neighbor, and Random Forest. These patterns are later used to detect future failures in other vehicles which show the similar behavior. The approach is produced with the end goal of expanding vehicle up-time and was demonstrated on 70 vehicles of Toyota Corolla type. Accuracy comparison of all classifiers is performed on the basis of Receiver Operating Characteristics (ROC) curves.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vehicle Remote Health Monitoring and Prognostic Maintenance System

Shafi

Safi

Raza

et al. 2018

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…Byttner et al [5] presented the consensus self-organised method that aims to find and select related sensor data on each vehicle to be used in detecting faults that are not predefined. The model is generated and adopted on an on-board system while the vehicle is being used.…”

Section: An Overview Of Phm System For Vehiclementioning

confidence: 99%

Overview of Telematics-Based Prognostics and Health Management Systems for Commercial Vehicles

Mesgarpour

Landa-Silva

Dickinson³

2013

Communications in Computer and Information Science

View full text Add to dashboard Cite

Abstract. Prognostics and Health Management/Monitoring (PHM) are methods to assess the health condition and reliability of systems for the purpose of maximising operational reliability and safety. Recently, PHM systems are emerging in the automotive industry. In the commercial vehicle sector, reducing the maintenance cost and downtime while also improving the reliability of vehicle components can have a major impact on fleet performance and hence business competitiveness. Nowadays, telematics and GPS are used mainly for fleet tracking and diagnostics purposes. Increased numbers of sensors installed on commercial vehicles, advancement of data analytics and computational intelligence methods, increased capabilities for on-board data processing as well as in the cloud, are creating an opportunity for PHM systems to be deployed on commercial vehicles and hence improve the overall operational efficiency. This paper surveys and analyses the nature of PHM as well as progress and challenges towards achieving integrated and intelligent PHM systems for commercial vehicles.

show abstract

“…al. [1], a method called COSMO is proposed for distributed search of "interesting relations" (e.g. strong linear correlations that hold for long periods of time) among on-board signals in a fleet of vehicles.…”

Section: Related Researchmentioning

confidence: 99%

Towards relation discovery for diagnostics

Prytz

Nowaczyk

Byttner

2011

Proceedings of the First International Workshop on Data Mining for Service and Maintenance

View full text Add to dashboard Cite

It is difficult to implement predictive maintenance in the automotive industry as it looks today, since the sensor capabilities and engineering effort available for diagnostic purposes is limited. It is, in practice, impossible to develop diagnostic algorithms capable of detecting many different kinds of faults that would be applicable to a wide range of vehicle configurations and usage patterns.However, it is now becoming feasible to obtain and analyse on-board data on vehicles as they are being used. It makes automatic data-mining methods an attractive alternative, since they are capable of adapting themselves to specific vehicle configurations and usage. In order to be useful, though, such methods need to be able to detect interesting relations between large number of available signal. This paper presents an unsupervised method for discovering useful relations between measured signals in a Volvo truck, both during normal operations and when a fault has occurred. The interesting relationships are found in a twostep procedure. In the first step, we identify a set of "good" models, by establishing an MSE threshold over the complete data set. In the second step, we estimate model parameters over time, in order to capture the dynamic behaviour of the system. We use two different approaches here, the LASSO method and the Recursive Least Squares filter. The usefulness of obtained relations is then evaluated using supervised learning to separate different classes of faults.

show abstract

Consensus self-organized models for fault detection (COSMO)

Cited by 42 publications

References 6 publications

Vehicle Remote Health Monitoring and Prognostic Maintenance System

Vehicle Remote Health Monitoring and Prognostic Maintenance System

Overview of Telematics-Based Prognostics and Health Management Systems for Commercial Vehicles

Towards relation discovery for diagnostics

Contact Info

Product

Resources

About