| 1. |
- Byttner, Stefan, 1975-, et al.
(författare)
-
A field test with self-organized modeling for knowledge discovery in a fleet of city buses
- 2013
-
Ingår i: 2013 IEEE International Conference on Mechatronics and Automation (ICMA 2013). - Piscataway, NJ : IEEE Press. - 9781467355605 - 9781467355575 - 9781467355582 - 9781467355599 ; , s. 896-901
-
Konferensbidrag (refereegranskat)abstract
- Fleets of commercial vehicles represent an excellent real life setting for ubiquitous knowledge discovery. There are many electronic control units onboard a modern bus or truck, with hundreds of signals being transmitted between them on the controller area network. The growing complexity of the vehicles has lead to a significant desire to have systems for fault detection, remote diagnostics and maintenance prediction. This paper aims to show that it is possible to discover useful diagnostic knowledge by a self-organized algorithm in the scenario of a fleet of city buses. The approach is demonstrated as a process consisting of two parts; Unsupervised modeling (where interesting features are discovered) and Guided search (where the previously found features are coupled to additional information sources). The modeling part searches for simple linear models in a group of vehicles, where interesting features are selected based on both non-randomness in relations and variability in the group. It is shown in an eight months long data collection study that this approach was able to discover features related to broken wheelspeed sensors. Strikingly, deviations in these features (for the vehicles with broken sensors) can be observed up to several months before a breakdown occur. This potentially allows for sufficient time to schedule the vehicle for maintenance and prepare the workshop with relevant components. © 2013 IEEE.
|
|
| 2. |
- Carpatorea, Iulian, 1982-, et al.
(författare)
-
APPES Maps as Tools for Quantifying Performance of Truck Drivers
- 2014
-
Ingår i: Proceedings of the 2014 International Conference on Data Mining, DMIN'14. - USA : CSREA Press. - 9781601323132 ; , s. 10-16
-
Konferensbidrag (refereegranskat)abstract
- Understanding and quantifying drivers’ influence on fuel consumption is an important and challenging problem. A number of commonly used approaches are based on collection of Accelerator Pedal Position - Engine Speed (APPES) maps. Up until now, however, most publicly available results are based on limited amounts of data collected in experiments performed under well-controlled conditions. Before APPES maps can be considered a reliable solution, there is a need to evaluate the usefulness of those models on a larger and more representative data.In this paper we present analysis of APPES maps that were collected, under actual operating conditions, on more than 1200 trips performed by a fleet of 5 Volvo trucks owned by a commercial transporter in Europe. We use Gaussian Mixture Models to identify areas of those maps that correspond to different types of driver behaviour, and investigate how the parameters of those models relate to variables of interest such as vehicle weight or fuel consumption.
|
|
| 3. |
- Carpatorea, Iulian, 1982-, et al.
(författare)
-
Towards Data Driven Method for Quantifying Performance of Truck Drivers
- 2014
-
Ingår i: The SAIS Workshop 2014 Proceedings. - : Swedish Artificial Intelligence Society (SAIS). ; , s. 133-142
-
Konferensbidrag (refereegranskat)abstract
- Understanding factors that influence fuel consumption is a very important task both for the OEMs in the automotive industry and for their customers. There is a lot of knowledge already available concerning this topic, but it is poorly organized and often more anecdotal than rigorously verified. Nowadays, however, rich datasets from actual vehicle usage are available and a data-mining approach can be used to not only validate earlier hypotheses, but also to discover unexpected influencing factors.In this paper we particularly focus on analyzing how behavior of drivers affects fuel consumption. To this end we introduce a concept of “Base Value”, a number that incorporates many constant, unmeasured factors. We show our initial results on how it allows us to categorize driver’s performance more accurately than previously used methods. We present a detailed analysis of 32 trips by Volvo trucks that we have selected from a larger database. Those trips have a large overlap in the route traveled, of over 100 km, and at the same time exhibit different driver and fuel consumption characteristics.
|
|
| 4. |
- Fan, Yuantao, 1989-, et al.
(författare)
-
Using Histograms to Find Compressor Deviations in Bus Fleet Data
- 2014
-
Ingår i: The SAIS Workshop 2014 Proceedings. - : Swedish Artificial Intelligence Society (SAIS). ; , s. 123-132
-
Konferensbidrag (refereegranskat)abstract
- Cost effective methods for predictive maintenance are increasingly demanded in the automotive industry. One solution is to utilize the on-board signals streams on each vehicle and build self-organizing systems that discover data deviations within a fleet. In this paper we evaluate histograms as features for describing and comparing individual vehicles. The results are based on a long-term field test with nineteen city buses operating around Kungsbacka in Halland. The purpose of this work is to investigate ways of discovering abnormal behaviors and irregularities between histograms of on-board signals, here specifically focusing on air pressure. We compare a number of distance measures and analyze the variability of histograms collected over different time spans. Clustering algorithms are used to discover structure in the data and track how this changes over time. As data are compared across the fleet, observed deviations should be matched against (often imperfect) reference data coming from workshop maintenance and repair databases.
|
|
| 5. |
- Haage, Mathias, et al.
(författare)
-
Declarative-knowledge-based reconfiguration of automation systems using a blackboard architecture
- 2011
-
Ingår i: Eleventh Scandinavian Conference on Artificial Intelligence. - Amsterdam : IOS Press. - 9781607507536 - 9781607507543 ; 227, s. 163-172
-
Bokkapitel (refereegranskat)abstract
- This article describes results of the work on knowledge representation techniques chosen for use in the European project SIARAS (Skill-Based Inspection and Assembly for Reconfigurable Automation Systems). Its goal was to create intelligent support system for reconfiguration and adaptation of robot-based manufacturing cells. Declarative knowledge is represented first of all in an ontology expressed in OWL, for a generic taxonomical reasoning, and in a number of special-purpose reasoning modules, specific for the application domain. The domain/dependent modules are organized in a blackboard-like architecture.
|
|
| 6. |
- Mashad Nemati, Hassan, 1982-, et al.
(författare)
-
Overview of Smart Grid Challenges in Sweden
- 2014
-
Ingår i: The SAIS Workshop 2014 Proceedings. - : Swedish Artificial Intelligence Society (SAIS). ; , s. 155-164
-
Konferensbidrag (refereegranskat)abstract
- Smart grids are advanced power grids that use modern hardware and software technologies to provide clean, safe, secure, reliable, ecient and sustainable energy. However, there are many challenges in the eld of smart grids in terms of communication, reliability, interoperability, and big data that should be considered. In this paper we present a brief overview of some of the challenges and solutions in the smart grids, focusing especially on the Swedish point of view. We discuss thirty articles, from 2006 until 2013, with the main interest on datarelated challenges.
|
|
| 7. |
- Nowaczyk, Sławomir, 1978-, et al.
(författare)
-
Ideas for Fault Detection Using Relation Discovery
- 2012
-
Konferensbidrag (refereegranskat)abstract
- Predictive maintenance is becoming more and more important in many industries, especially taking into account the increasing focus on offering uptime guarantees to the customers. However, in automotive industry, there is a limitation on the engineering effort and sensor capabilities available for that purpose. Luckily, it has recently become feasible to analyse large amounts of data on-board vehicles in a timely manner. This allows approaches based on data mining and pattern recognition techniques to augment existing, hand crafted algorithms.Automated deviation detection offers both broader applicability, by virtue of detecting unexpected faults and cross-analysing data from different subsystems, as well as higher sensitivity, due to its ability to take into account specifics of a selected, small set of vehicles used in a particular way under similar conditions.In a project called Redi2Service we work towards developing methods for autonomous and unsupervised relationship discovery, algorithms for detecting deviations within those relationships (both considering different moments in time, and different vehicles in a fleet), as well as ways to correlate those deviations to known and unknown faults. In this paper we present the type of data we are working with, justify why we believe relationships between signals are a good knowledge representation, and show results of early experiments where supervised learning was used to evaluate discovered relations.
|
|
| 8. |
- Nowaczyk, Sławomir, 1978-, et al.
(författare)
-
Towards a Machine Learning Algorithm for Predicting Truck Compressor Failures Using Logged Vehicle Data
- 2013
-
Ingår i: Twelfth Scandinavian Conference on Artificial Intelligence. - Amsterdam : IOS Press. - 9781614993308 - 9781614993292 ; , s. 205-214
-
Konferensbidrag (refereegranskat)abstract
- Predictive maintenance is becoming more and more important for the commercial vehicle manufactures, as focus shifts from product- to service-based operation. The idea is to provide a dynamic maintenance schedule, fulfilling specific needs of individual vehicles. Luckily, the same shift of focus, as well as technological advancements in the telecommunication area, make long-term data collection more widespread, delivering the necessary data.We have found, however, that the standard attribute-value knowledge representation is not rich enough to capture important dependencies in this domain. Therefore, we are proposing a new rule induction algorithm, inspired by Michalski's classical AQ approach. Our method is aware that data concerning each vehicle consists of time-ordered sequences of readouts. When evaluating candidate rules, it takes into account the composite performance for each truck, instead of considering individual readouts in separation. This allows us more exibility, in particular in defining desired prediction horizon in a fuzzy, instead of crisp, manner. © 2013 The authors and IOS Press. All rights reserved.
|
|
| 9. |
- Prytz, Rune, 1980-, et al.
(författare)
-
Analysis of Truck Compressor Failures Based on Logged Vehicle Data
- 2013
-
Konferensbidrag (refereegranskat)abstract
- In multiple industries, including automotive one, predictive maintenance is becoming more and more important, especially since the focus shifts from product to service-based operation. It requires, among other, being able to provide customers with uptime guarantees. It is natural to investigate the use of data mining techniques, especially since the same shift of focus, as well as technological advancements in the telecommunication solutions, makes long-term data collection more widespread.In this paper we describe our experiences in predicting compressor faults using data that is logged on-board Volvo trucks. We discuss unique challenges that are posed by the specifics of the automotive domain. We show that predictive maintenance is possible and can result in significant cost savings, despite the relatively low amount of data available. We also discuss some of the problems we have encountered by employing out-of-the-box machine learning solutions, and identify areas where our task diverges from common assumptions underlying the majority of data mining research.
|
|
| 10. |
- Prytz, Rune, et al.
(författare)
-
Towards relation discovery for diagnostics
- 2011
-
Ingår i: Proceedings of the First International Workshop on Data Mining for Service and Maintenance. - New York, NY : Association for Computing Machinery (ACM). - 9781450308427 ; , s. 23-27
-
Konferensbidrag (refereegranskat)abstract
- 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 a large number of available signals. 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 two-step 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.
|
|
