Slawomir Nowaczyk

Knowledge-Based Reconfiguration of Automation Systems

This article describes the work in progress on knowledge-based reconfiguration of a class of automation systems. The knowledge about manufacturing is represented in a number of formalisms and gathered around an ontology expressed in OWL, that allows generic reasoning in description logic. In the same time multiple representations facilitate efficient processing by a number of special-purpose reasoning modules, specific for the application domain. At the final stage of reconfiguration we exploit ontology-based rewriting, simplifying creation of the final configuration files.

Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data

Methods and results are presented for applying supervised machine learning techniques to the task of predicting the need for repairs of air compressors in commercial trucks and buses. Prediction models are derived from logged on-board data that are downloaded during workshop visits and have been collected over three years on a large number of vehicles. A number of issues are identified with the data sources, many of which originate from the fact that the data sources were not designed for data mining. Nevertheless, exploiting this available data is very important for the automotive industry as means to quickly introduce predictive maintenance solutions. It is shown on a large data set from heavy duty trucks in normal operation how this can be done and generate a profit.Random forest is used as the classifier algorithm, together with two methods for feature selection whose results are compared to a human expert. The machine learning based features outperform the human expert features, which supports the idea to use data mining to improve maintenance operations in this domain.

Declarative-knowledge-based reconfiguration of automation systems using a blackboard architecture

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.

Evaluation of Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet☆

Managing the maintenance of a commercial vehicle fleet is an attractive application domain of ubiquitous knowledge discovery. Cost effective methods for predictive maintenance are progressively demanded in the automotive industry. The traditional diagnostic paradigm that requires human experts to define models is not scalable to todays vehicles with hundreds of computing units and thousands of control and sensor signals streaming through the on-board controller area network. A more autonomous approach must be developed. In this paper the authors evaluate the performance of the COSMO approach for automatic detection of air pressure related faults on a fleet of city buses. The method is both generic and robust. Histograms of a single pressure signal are collected and compared across the fleet and deviations are matched against workshop maintenance and repair records. It is shown that the method can detect several of the cases when compressors fail on the road, well before the failure. The work is based on data from a three year long field study involving 19 buses operating in and around a city on the west coast of Sweden.

On using rule induction in multiple classifiers with a combiner aggregation strategy

The paper is an experimental study of using the rough sets based rule induction algorithm MODLEM in the framework of multiple classifiers. Particular attention is paid to using a meta-classifier called combiner, which learns how to aggregate answers of component classifiers. The experimental results confirm that the range of classification improvement for the combiner depends on the independence of errors made by the component classifiers. Moreover, we summarize the experience with using MODLEM in other multiple classifiers, namely the bagging and n/sup 2/ classifiers.

A field test with self-organized modeling for knowledge discovery in a fleet of city buses

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.

Mode tracking using multiple data streams

Most existing work in information fusion focuses on combining information with well-defined meaning towards a concrete, pre-specified goal. In contradistinction, we instead aim for autonomous disco ...

Learning of Aggregate Features for Comparing Drivers Based on Naturalistic Data

Fuel used by heavy duty trucks is a major cost for logistics companies, and therefore improvements in this area are highly desired. Many of the factors that influence fuel consumption, such as the road type, vehicle configuration or external environment, are difficult to influence. One of the most under-explored ways to lower the costs is training and incentivizing drivers. However, today it is difficult to measure driver performance in a comprehensive way outside of controlled, experimental setting. This paper proposes a machine learning methodology for quantifying and qualifying driver performance, with respect to fuel consumption, that is suitable for naturalistic driving situations. The approach is a knowledge-based feature extraction technique, constructing a normalizing fuel consumption value denoted Fuel under Predefined Conditions (FPC), which captures the effect of factors that are relevant but are not measured directly. The FPC, together with information available from truck sensors, is then compared against the actual fuel used on a given road segment, quantifying the effects associated with driver behavior or other variables of interest. We show that raw fuel consumption is a biased measure of driver performance, being heavily influenced by other factors such as high load or adversary weather conditions, and that using FPC leads to more accurate results. In this paper we also show evaluation the proposed method using large-scale, real-world, naturalistic database of heavy-duty vehicle operation.

Incorporating Expert Knowledge into a Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet

In the automotive industry, cost effective methods for predictive maintenance are increasingly in demand. The traditional approach for developing diagnostic methods on commercial vehicles is heavily based on knowledge of human experts, and thus it does not scale well to modern vehicles with many components and subsystems. In previous work we have presented a generic self-organising approach called COSMO that can detect, in an unsupervised manner, many different faults. In a study based on a commercial fleet of 19 buses operating in Kungsbacka, we have been able to predict, for example, fifty percent of the compressors that break down on the road, in many cases weeks before the failure.In this paper we compare those results with a state of the art approach currently used in the industry, and we investigate how features suggested by experts for detecting compressor failures can be incorporated into the COSMO method. We perform several experiments, using both real and synthetic data, to identify issues that need to be considered to improve the accuracy. The final results show that the COSMO method outperforms the expert method.

Towards a Machine Learning Algorithm for Predicting Truck Compressor Failures Using Logged Vehicle Data

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 mainte ...