| 1. |
- Biteus, Jonas, et al.
(författare)
-
Planning Flexible Maintenance for Heavy Trucks using Machine Learning Models, Constraint Programming, and Route Optimization
- 2017
-
Ingår i: SAE International Journal of Materials & Manufacturing. - : SAE International. - 1946-3979 .- 1946-3987. ; 10:3, s. 306-315
-
Tidskriftsartikel (refereegranskat)abstract
- Maintenance planning of trucks at Scania have previously been done using static cyclic plans with fixed sets of maintenance tasks, determined by mileage, calendar time, and some data driven physical models. Flexible maintenance have improved the maintenance program with the addition of general data driven expert rules and the ability to move sub-sets of maintenance tasks between maintenance occasions. Meanwhile, successful modelling with machine learning on big data, automatic planning using constraint programming, and route optimization are hinting on the ability to achieve even higher fleet utilization by further improvements of the flexible maintenance. The maintenance program have therefore been partitioned into its smallest parts and formulated as individual constraint rules. The overall goal is to maximize the utilization of a fleet, i.e. maximize the ability to perform transport assignments, with respect to maintenance. A sub-goal is to minimize costs for vehicle break downs and the costs for maintenance actions. The maintenance planner takes as input customer preferences and maintenance task deadlines where the existing expert rule for the component has been replaced by a predictive model. Using machine learning, operational data have been used to train a predictive random forest model that can estimate the probability that a vehicle will have a breakdown given its operational data as input. The route optimization takes predicted vehicle health into consideration when optimizing routes and assignment allocations. The random forest model satisfactory predicts failures, the maintenance planner successfully computes consistent and good maintenance plans, and the route optimizer give optimal routes within tens of seconds of operation time. The model, the maintenance planner, and the route optimizer have been integrated into a demonstrator able to highlight the usability and feasibility of the suggested approach.
|
|
| 2. |
- Boström, Henrik, et al.
(författare)
-
Conformal prediction using random survival forests
- 2017
-
Ingår i: Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538614174 ; , s. 812-817, s. 812-817
-
Konferensbidrag (refereegranskat)abstract
- Random survival forests constitute a robust approach to survival modeling, i.e., predicting the probability that an event will occur before or on a given point in time. Similar to most standard predictive models, no guarantee for the prediction error is provided for this model, which instead typically is empirically evaluated. Conformal prediction is a rather recent framework, which allows the error of a model to be determined by a user specified confidence level, something which is achieved by considering set rather than point predictions. The framework, which has been applied to some of the most popular classification and regression techniques, is here for the first time applied to survival modeling, through random survival forests. An empirical investigation is presented where the technique is evaluated on datasets from two real-world applications; predicting component failure in trucks using operational data and predicting survival and treatment of heart failure patients from administrative healthcare data. The experimental results show that the error levels indeed are very close to the provided confidence levels, as guaranteed by the conformal prediction framework, and that the error for predicting each outcome, i.e., event or no-event, can be controlled separately. The latter may, however, lead to less informative predictions, i.e., larger prediction sets, in case the class distribution is heavily imbalanced.
|
|
| 3. |
- Boström, Henrik, et al.
(författare)
-
Explaining Random Forest Predictions with Association Rules
- 2018
-
Ingår i: Archives of Data Science. - 2363-9881. ; 5:1, s. 1-20
-
Tidskriftsartikel (refereegranskat)abstract
- Random forests frequently achieve state-of-the-art predictive performance. However, the logic behind their predictions cannot be easily understood, since they are the result of averaging often hundreds or thousands of, possibly conflicting, individual predictions. Instead of presenting all the individual predictions, an alternative is proposed, by which the predictions are explained using association rules generated from itemsets representing paths in the trees of the forest. An empirical investigation is presented, in which alternative ways of generating the association rules are compared with respect to explainability, as measured by the fraction of predictions for which there is no applicable rule and by the fraction of predictions for which there is at least one applicable rule that conflicts with the forest prediction. For the considered datasets, it can be seen that most predictions can be explained by the discovered association rules, which have a high level of agreement with the underlying forest. The results do not single out a clear winner of the considered alternatives in terms of unexplained and disagreement rates, but show that they are associated with substantial differences in computational cost.
|
|
| 4. |
- Bull, L. A., et al.
(författare)
-
Hierarchical Bayesian modeling for knowledge transfer across engineering fleets via multitask learning
- 2023
-
Ingår i: Computer-Aided Civil and Infrastructure Engineering. - : Wiley. - 1093-9687 .- 1467-8667. ; 38:7, s. 821-848
-
Tidskriftsartikel (refereegranskat)abstract
- A population-level analysis is proposed to address data sparsity when building predictive models for engineering infrastructure. Utilizing an interpretable hierarchical Bayesian approach and operational fleet data, domain expertise is naturally encoded (and appropriately shared) between different subgroups, representing (1) use-type, (2) component, or (3) operating condition. Specifically, domain expertise is exploited to constrain the model via assumptions (and prior distributions) allowing the methodology to automatically share information between similar assets, improving the survival analysis of a truck fleet (15% and 13% increases in predictive log-likelihood of hazard) and power prediction in a wind farm (up to 82% reduction in the standard deviation of maximum output prediction). In each asset management example, a set of correlated functions is learnt over the fleet, in a combined inference, to learn a population model. Parameter estimation is improved when subfleets are allowed to share correlated information at different levels in the hierarchy; the (averaged) reduction in standard deviation for interpretable parameters in the survival analysis is 70%, alongside 32% in wind farm power models. In turn, groups with incomplete data automatically borrow statistical strength from those that are data-rich. The statistical correlations enable knowledge transfer via Bayesian transfer learning, and the correlations can be inspected to inform which assets share information for which effect (i.e., parameter). Successes in both case studies demonstrate the wide applicability in practical infrastructure monitoring, since the approach is naturally adapted between interpretable fleet models of different in situ examples.
|
|
| 5. |
- Dhada, Maharshi, et al.
(författare)
-
Weibull recurrent neural networks for failure prognosis using histogram data
- 2023
-
Ingår i: Neural Computing & Applications. - : Springer Science and Business Media LLC. - 0941-0643 .- 1433-3058. ; 35:4, s. 3011-3024
-
Tidskriftsartikel (refereegranskat)abstract
- Weibull time-to-event recurrent neural networks (WTTE-RNN) is a simple and versatile prognosis algorithm that works by optimising a Weibull survival function using a recurrent neural network. It offers the combined benefits of the sequential nature of the recurrent neural network, and the ability of the Weibull loss function to incorporate censored data. The goal of this paper is to present the first industrial use case of WTTE-RNN for prognosis. Prognosis of turbocharger conditions in a fleet of heavy-duty trucks is presented here, where the condition data used in the case study were recorded as a time series of sparsely sampled histograms. The experiments include comparison of the prediction models trained using data from the entire fleet of trucks vs data from clustered sub-fleets, where it is concluded that clustering is only beneficial as long as the training dataset is large enough for the model to not overfit. Moreover, the censored data from assets that did not fail are also shown to be incorporated while optimising the Weibull loss function and improve prediction performance. Overall, this paper concludes that WTTE-RNN-based failure predictions enable predictive maintenance policies, which are enhanced by identifying the sub-fleets of similar trucks.
|
|
| 6. |
- Gurung, Ram B., et al.
(författare)
-
An Interactive Visual Tool Enhance Understanding of Random Forest Prediction
- 2020
-
Ingår i: Archives of Data Science, Series A. - 2363-9881. ; 6:1
-
Tidskriftsartikel (refereegranskat)abstract
- Random forests are known to provide accurate predictions, but the predictions are not easy to understand. In order to provide support for understanding such predictions, an interactive visual tool has been developed. The tool can be used to manipulate selected features to explore what-if scenarios. It exploits the internal structure of decision trees in a trained forest model and presents these information as interactive plots and charts. In addition, the tool presents a simple decision rule as an explanation for the prediction. It also presents the recommendation for reassignments of feature values of the example that leads to change in the prediction to a preferred class. An evaluation of the tool was undertaken in a large truck manufacturing company, targeting a fault prediction of a selected component in trucks. A set of domain experts were invited to use the tool and provide feedback in post-task interviews. The result of this investigation suggests that the tool indeed may aid in understanding the predictions of random forest, and also allows for gaining new insights.
|
|
| 7. |
- Gurung, Ram Bahadur, 1983-
(författare)
-
Learning Decision Trees and Random Forests from Histogram Data : An application to component failure prediction for heavy duty trucks
- 2017
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A large volume of data has become commonplace in many domains these days. Machine learning algorithms can be trained to look for any useful hidden patterns in such data. Sometimes, these big data might need to be summarized to make them into a manageable size, for example by using histograms, for various reasons. Traditionally, machine learning algorithms can be trained on data expressed as real numbers and/or categories but not on a complex structure such as histogram. Since machine learning algorithms that can learn from data with histograms have not been explored to a major extent, this thesis intends to further explore this domain.This thesis has been limited to classification algorithms, tree-based classifiers such as decision trees, and random forest in particular. Decision trees are one of the simplest and most intuitive algorithms to train. A single decision tree might not be the best algorithm in term of its predictive performance, but it can be largely enhanced by considering an ensemble of many diverse trees as a random forest. This is the reason why both algorithms were considered. So, the objective of this thesis is to investigate how one can adapt these algorithms to make them learn better on histogram data. Our proposed approach considers the use of multiple bins of a histogram simultaneously to split a node during the tree induction process. Treating bins simultaneously is expected to capture dependencies among them, which could be useful. Experimental evaluation of the proposed approaches was carried out by comparing them with the standard approach of growing a tree where a single bin is used to split a node. Accuracy and the area under the receiver operating characteristic (ROC) curve (AUC) metrics along with the average time taken to train a model were used for comparison. For experimental purposes, real-world data from a large fleet of heavy duty trucks were used to build a component-failure prediction model. These data contain information about the operation of trucks over the years, where most operational features are summarized as histograms. Experiments were performed further on the synthetically generated dataset. From the results of the experiments, it was observed that the proposed approach outperforms the standard approach in performance and compactness of the model but lags behind in terms of training time. This thesis was motivated by a real-life problem encountered in the operation of heavy duty trucks in the automotive industry while building a data driven failure-prediction model. So, all the details about collecting and cleansing the data and the challenges encountered while making the data ready for training the algorithm have been presented in detail.
|
|
| 8. |
- Gurung, Ram B., et al.
(författare)
-
Learning Decision Trees from Histogram Data
- 2015
-
Ingår i: Proceedings of the 2015 International Conference on Data Mining. - : AAAI Press. - 9781601324030 ; , s. 139-145
-
Konferensbidrag (refereegranskat)abstract
- When applying learning algorithms to histogram data, bins of such variables are normally treated as separate independent variables. However, this may lead to a loss of information as the underlying dependencies may not be fully exploited. In this paper, we adapt the standard decision tree learning algorithm to handle histogram data by proposing a novel method for partitioning examples using binned variables. Results from employing the algorithm to both synthetic and real-world data sets demonstrate that exploiting dependencies in histogram data may have positive effects on both predictive performance and model size, as measured by number of nodes in the decision tree. These gains are however associated with an increased computational cost and more complex split conditions. To address the former issue, an approximate method is proposed, which speeds up the learning process substantially while retaining the predictive performance.
|
|
| 9. |
- Gurung, Ram B., et al.
(författare)
-
Learning Decision Trees from Histogram Data Using Multiple Subsets of Bins
- 2016
-
Ingår i: Proceedings of the Twenty-Ninth International Florida Artificial Intelligence Research Society Conference. - : AAAI Press. - 9781577357568 ; , s. 430-435
-
Konferensbidrag (refereegranskat)abstract
- The standard approach of learning decision trees from histogram data is to treat the bins as independent variables. However, as the underlying dependencies among the bins might not be completely exploited by this approach, an algorithm has been proposed for learning decision trees from histogram data by considering all bins simultaneously while partitioning examples at each node of the tree. Although the algorithm has been demonstrated to improve predictive performance, its computational complexity has turned out to be a major bottleneck, in particular for histograms with a large number of bins. In this paper, we propose instead a sliding window approach to select subsets of the bins to be considered simultaneously while partitioning examples. This significantly reduces the number of possible splits to consider, allowing for substantially larger histograms to be handled. We also propose to evaluate the original bins independently, in addition to evaluating the subsets of bins when performing splits. This ensures that the information obtained by treating bins simultaneously is an additional gain compared to what is considered by the standard approach. Results of experiments on applying the new algorithm to both synthetic and real world datasets demonstrate positive results in terms of predictive performance without excessive computational cost.
|
|
| 10. |
- Gurung, Ram B., et al.
(författare)
-
Learning Random Forest from Histogram Data Using Split Specific Axis Rotation
- 2018
-
Ingår i: International Journal of Machine Learning and Computing. - : EJournal Publishing. - 2010-3700. ; 8:1, s. 74-79
-
Tidskriftsartikel (refereegranskat)abstract
- Machine learning algorithms for data containing histogram variables have not been explored to any major extent. In this paper, an adapted version of the random forest algorithm is proposed to handle variables of this type, assuming identical structure of the histograms across observations, i.e., the histograms for a variable all use the same number and width of the bins. The standard approach of representing bins as separate variables, may lead to that the learning algorithm overlooks the underlying dependencies. In contrast, the proposed algorithm handles each histogram as a unit. When performing split evaluation of a histogram variable during tree growth, a sliding window of fixed size is employed by the proposed algorithm to constrain the sets of bins that are considered together. A small number of all possible set of bins are randomly selected and principal component analysis (PCA) is applied locally on all examples in a node. Split evaluation is then performed on each principal component. Results from applying the algorithm to both synthetic and real world data are presented, showing that the proposed algorithm outperforms the standard approach of using random forests together with bins represented as separate variables, with respect to both AUC and accuracy. In addition to introducing the new algorithm, we elaborate on how real world data for predicting NOx sensor failure in heavy duty trucks was prepared, demonstrating that predictive performance can be further improved by adding variables that represent changes of the histograms over time.
|
|
