Predicting NOx sensor failure in heavy duty trucks using histogram-based random forests

Abstract

Being able to accurately predict the impending failures of truck components is often associated with significant amount of cost savings, customer satisfaction and flexibility in maintenanceservice plans. However, because of the diversity in the way trucks typically are configured and their usage under different conditions, the creation of accurate prediction models is not an easy task. This paper describes an effort in creating such a prediction model for the NOx sensor, i.e., a component measuring the emitted level of nitrogen oxide in the exhaust of the engine. This component was chosen because it is vital for the truck to function properly, while at the same time being very fragile and costly to repair. As input to the model, technical specifications of trucks and their operational data are used. The process of collecting the data and making it ready for training the model via a slightly modified Random Forest learning algorithm is described along with various challenges encountered during this process. The operational data consists of features represented as histograms, posing an additional challenge for the data analysis task. In the study, a modified version of the random forest algorithm is employed, which exploits the fact that the individual bins in the histograms are related, in contrast to the standard approach that would consider the bins as independent features. Experiments are conducted using the updated random forest algorithm, and they clearly show that the modified version is indeed beneficial when compared to the standard random forest algorithm. The performance of the resulting prediction model for the NOx sensor is promising and may be adopted for the benefit of operators of heavy trucks.

Weibull recurrent neural networks for failure prognosis using histogram data

“…Asset condition data are often recorded by the industries as histograms, due to their memory efficiency and homogeneity across the variables [13,24]. Formally, the histogram data used for experiments in this paper are categorical histogram data, where a frequency is assigned to each bin [7].…”

“…These include the works of [9,32], and [12][13][14] who investigated compressor, battery failures, and NOx sensor failures in heavy-duty trucks, respectively. [9] did not clearly outline the preprocessing steps while using the histogram data, and the study of [32] was limited by the small fleet size used for analysis.…”

“…[9] did not clearly outline the preprocessing steps while using the histogram data, and the study of [32] was limited by the small fleet size used for analysis. The closest of the above three works to the case study discussed in this paper is that of [12][13][14] who used a dataset very similar to the one used here, but for a different prognosis technique and target component. The authors in [12][13][14] used random forest algorithm for classifying the data corresponding to failure and non-failure class.…”

“…The closest of the above three works to the case study discussed in this paper is that of [12][13][14] who used a dataset very similar to the one used here, but for a different prognosis technique and target component. The authors in [12][13][14] used random forest algorithm for classifying the data corresponding to failure and non-failure class. However, their technique relied on recursively combining the histogram bins using a sliding window, in contrast to the entropy of the histogram distributions used in this paper.…”