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DTC Prediction & Analysis

Data from the vehicle can be very useful for predicting faults and errors in the vehicle, but most of the data which comes from the sensors is redundant data. In this paper Diagnostic Trouble Code (DTC) in a vehicle is being predicted along with, eliminating the issue of redundant data.

The data used in my project was from Eicher's heavy-duty trucks. Some of them have sensors installed in them, which sends a combination of vehicle sensor data and vehicle data. Before feeding this data to Deep Neural Network (DNN), data is divided into multiple clusters based on the requirement which will be discussed in a later section of this paper. These clusters are then pre-processed in-order to remove redundancy from the data, as much as possible. Once the dataset is clean and ready to use, the important features are extracted from the data to feed into the machine learning model.

Feature extraction can be done using a method called wrapper method. Wrapper method is used to find out the number of minimum features that are most significant in predicting the output. The wrapper method takes all features as input parameters, a machine learning model for prediction, scoring technique (in this case r-square), and a significance level (default 0.05). For instance, if we are using a linear regression model, then it will calculate the p-value for each feature, which is also the null hypothesis. If we are using a backward elimination technique in the wrapper method, then it will eliminate a feature if its p-value is greater than the significance level.

Once we have all the required features, we applied 4 different models to predict the output. The models which we have used in this paper are Decision Tree, Random Forest Regressor, Logistic Regression, and Deep Neural Network. The reason behind using Decision Tree, Random Forest Regressor, and Logistic Regression along with DNN are, these models also work well on this kind of dataset and we can use it to compare results with each other.

The outcome of anything depends eventually upon the results which are generated from it. In this section, we will discuss the result for each phase of the project. Starting from data collection to model prediction. As each step is dependent on one another and gives crucial information about the data. But a certain part of each stage is independent of each other. Hence, it is important to know about the results of each stage. Starting from the data collection, we take raw data from the sensor and pre-process it in a way so that we can use it for DTC predicting and further use it for analysis of various other things as well. Therefore, the result of the initial part of the project is data, loaded with valuable information, which the organization can use as they want. 

The next part was based on the prediction of DTC. As stated earlier that we used 4 different models and compared the output of each model with one another. Amongst which Deep Neural Network (DNN) was producing the best result. This is because DNN can easily identify patterns and meaningful information from the dataset if the data are sufficient.

Once the model has successfully started predicting DTCs, this information can be used in many places. For instance, if there is a critical error or fault which might occur in a vehicle, the driver can be alerted in advance. This way, any major damage to the vehicle can be avoided. Such prediction is very helpful and beneficial for the organization, customer, and the dealer.

This project has helped me in many ways. I got to learn a lot from it. Also, it provided me with a platform to test my skills in machine learning and data analytics. The main learning outcome of this project is listed below:

§  I got to learn about real-world data. How it is generated and transmitted from the vehicle to the server.

§  I learnt about the different models of vehicle and its parts.

§  Attain more clarity on data mining and data pre-processing.

§  Learnt about the different data and their relationship with each other.

§  Got a better understanding of machine learning models.

§  Learnt about the visualizing techniques in Python using Matplotlib.

§  Learnt, how to handle big data using python and techniques to work upon it.

§  Learnt more about Deep Neural Network (DNN) and its layers.