Automobile Domain Research Articles

DyClee-N&C: a clustering algorithm for heterogeneous data based situation assessment

In data-based situation assessment applications, the proliferation of data acquired and recorded on current technological systems is a key issue in that data remain unlabeled because labeling would require too much time and implies prohibitive costs. The data should therefore speak for itself. The different situations, e.g., normal or faulty, must hence be learned only from the data. Clustering methods, also named unsupervised classification methods, can be used for that purpose. These methods are designed to cluster the samples according to some similarity criterion. The different clusters can be associated to different situations whose discrimination may be relevant to obtain a proper diagnosis.Numerous algorithms have been developed in recent years for clustering numeric data but these methods are not applicable to categorical data. This is the case of the algorithm DyClee, named DyClee-N in the paper. However, in many application domains, qualitative features are key to properly describe the different situations. DyClee-N was recast to produce a version, named DyClee-C that accepts categorical features, but only categorical features. This paper presents DyClee-N&C that subsumes both the numeric and categorical feature based algorithms DyClee-N and DyClee-C respectively. DyClee-N&C is applied to a data set of the literature for the evaluation of risk in the automobile domain and compared to state of the art clustering methods.

Application of Alternative Methodology to Select the Optimum Design of TCU Carrier Using FEA

<div class="section abstract"><div class="htmlview paragraph">The transmission control unit is used in automobile domain, to control the automatic transmissions. TCU is associated with mechanical parts like Housing, Carrier, Hydraulic Plate etc. In this case, TCU carrier is mounted on hydraulic plate using a dowel pin to locate it accurately. During servicing of the TCU, Carrier with dowel pin is subjected to high displacement which leads to huge plastic deformation & failure of the carrier. A study was required using Finite Element Analysis for a series of designs to evaluate their structural stability and to recommend the best possible design. ANSYS tool is used for the simulation. During this study, when conventional method of reckoning stresses and strains as a criterion of comparison was adopted, it involved a tedious process & required many numbers of iterations. Despite plenty of iterations, it may not be possible to conclude the exact force at which the carrier fails or the max displacement it can withstand. The other reason to look for an alternative method is, the development team expectations about the structural stability of the carrier considering operating conditions is contradicting with the strains observed with conventional method. So, there was a need to develop alternative method to evaluate the carrier strength. Hence, stiffness criterion is obtained as a solution. Series of designs are compared based on stiffness & deformation criterion for loads ranging from operating load of 3 mm to maximum displacement load of 6 mm. The simulation results through this stiffness method obtained are also validated with testing results and accuracy of results is observed. This study using stiffness method helped us in selection of best possible carrier design suitable for maximum displacement load without any failure. Cost of testing for around 6 designs is reduced by using FEA and optimum design is also obtained.</div></div>

Kano and QFD analyses for autonomous electric car: Design for enhancing customer contentment

With the development of new technologies, the services in the automobile sector are rapidly changing. In the ancient times, the manually-driven wheel laid the foundation of transport; while now the world is heading towards self-driving vehicles. These vehicles provide contentment to the customer behaviour through the means of their features like voice-bot and intelligent interface. Moreover, with the introduction of micro-computers, large databases for maintaining such autonomous vehicles can be handled easily. This paper presents a collective study of Kano model analysis and quality function deployment (QFD) in a detailed manner. Different attributes were provided to the potential customers regarding an autonomous electric car in a survey form. The consumers were asked to express their feelings regarding the product if a particular feature is provided and not provided in the product for conducting Kano analysis. Furthermore, QFD was implemented to determine the priority for making improvements in the product profile design. The attributes were mapped to technical metrics/parameters and ranked based on the target customers’ priorities. The output is displayed in a typical house of quality which also consisted of a benchmarking on market competitors. Overall, the paper provides a scientific and engineered framework regarding the attributes which may help the manufacturing organizations re-evaluate their services and come up with efficient and new technological features in the automobile domain.

A Semantic Similarity Model to Compare Heterogeneous Data Sources to Augment Engineering Data with New Failure modes in Automotive Industry

In real life industry, in-time exposure of symptoms and their failure modes observed during fault events provide key signals to system engineers to take necessary corrective actions for effectively arresting product defects. A real-life ontology-based semantic similarity system is employed for automatic comparison of engineering data (comes in the form of design failure mode effect analysis, DFMEA data) with the field repair data collected during product warranty period. Given the complexity of engineering data and the overwhelming volume (hundreds of millions of data points) of field repair data makes identification of new symptoms and failure modes from the first principles an impractical task. Typically, the engineering data is recorded by using technical vocabulary, e.g. unstable electric contact, Seat Belt comfort per MVSS208, whereas the field repair data is highly unstructured in nature. Consequently, we observe following types of noises in the field repair data – abbreviated text entries, inconsistent use of vocabulary (‘seat buckle is damaged’ vs ‘buckle unlatching’), and finally the incomplete text entries. More importantly, limited mental mapping capacity of a human agent limits discovery of new symptoms and failure modes from the industrial scale data. Not surprisingly, the text mining and semantic similarity are gaining a serious attention due to their ability to automatically discover the knowledge assets buried in unstructured text by training machines to compare and link high volume of data. In our approach, initially the key constructs (e.g. symptoms, failure modes) from the data are annotated by using the domain ontology. These constructs are then used to construct pairs of terms and pairs of tuples, which are used to compute pair-to-pair and tuple-to-tuple semantic similarity respectively. Finally, the text-to-text semantic similarity is calculated by combining other two semantic similarity scores. It is used to determine whether new symptoms or failure modes from the field repair data can be used to augment the DFMEA data. The proposed method is implemented as a prototype tool and its performance is validated by using real-life data from automobile domain. On an average, our system has F1 score of 0.75 and 0.78 in discovering and identifying new symptoms and synonym symptoms respectively, whereas it achieved the F1 score of 0.72 and 0.68 in discovering new failure modes and in identifying synonym failure modes respectively. The fault detection rate is improved by 35%, whereas the fault isolation rate is improved by 40.5%.

D-Matrix: Fault Diagnosis Framework

Fault dependency (D-matrix) is a systematic diagnostic model which is used to catch the fault system data and its causal relationship at the hierarchical system-level. It consists of dependencies and relationship between observable failure modes and symptoms associated with a system. Proposed system describes an ontology based text mining method for automatically constructing and updating a D-matrix by mining thousands of repair verbatim data (typically written in unstructured text) collected during the diagnosis episodes. First we construct fault diagnosis ontology and then employ text mining method to identify dependencies. The proposed method will be implemented as a prototype tool and validated by using real-life data collected from the automobile domain.

An Ontology-Based Text Mining Method to Develop D-Matrix From Unstructured Text

Fault dependency (D)-matrix is a systematic diagnostic model [7] to capture the hierarchical system-level fault diagnostic information consisting of dependencies between observable symptoms and failure modes associated with a system. Constructing a D-matrix from first principles and updating it using the domain knowledge is a labor intensive and time consuming task. Further, in-time augmentation of D-matrix through the discovery of new symptoms and failure modes observed for the first time is a challenging task. Here, we describe an ontology-based text mining method for automatically constructing and updating a D-matrix by mining hundreds of thousands of repair verbatim (typically written in unstructured text) collected during the diagnosis episodes. In our approach, we first construct the fault diagnosis ontology consisting of concepts and relationships commonly observed in the fault diagnosis domain. Next, we employ the text mining algorithms that make use of this ontology to identify the necessary artifacts, such as parts, symptoms, failure modes, and their dependencies from the unstructured repair verbatim text. The proposed method is implemented as a prototype tool and validated by using real-life data collected from the automobile domain.

Product Design for the Long Run: Consumer Responses to Typical and Atypical Designs at Different Stages of Exposure

Extant research on product design has suggested that a design's typicality is an important determinant of consumers' aesthetic liking. Yet most studies to date have measured consumers' reactions to designs of varied typicality after a single exposure. In reality, however, consumers usually have multiple opportunities to observe a product before making a decision. Against this background, the authors perform three studies in the automobile domain that examine whether the positive effect of design typicality is moderated by the level of exposure. Study 1 indicates that aesthetic liking of typical car designs is greater at lower exposure levels, whereas people like atypical car designs better at higher exposure levels. Study 2 uses real sales data and indicates that the interaction between design typicality and exposure also affects sales, suggesting that atypical cars may be more successful in the long run. Using experimental manipulations of the key constructs, Study 3 provides evidence for the underlying process and finds renewed support for the notion that design typicality and exposure interact to affect aesthetic liking.

H-infinity control and μ-analysis for active control of vehicle longitudinal oscillations

In the automobile domain, the flexibility of the drivelines is such that unpleasant behaviors of the vehicle during transient may occur. After introducing a simplified mechanical model, the paper deals with the problem of active damping of the low frequency longitudinal car oscillations. Both control design and analysis are considered. An H∞ based methodology is first presented which requires two tuning parameters with independent effects. Making use of the reduced mechanical model, the influence of parametric uncertainties are then quantified thanks to μ-analysis.