Automobile Failure Research Articles

Knowledge Management for Design Development and Maintenance through Text Mining of Product Information

In recent years, Japanese manufacturing industry has been facing a skill succession problem due to the retirement of skilled workers and a shortage of young workers, despite the increasing complexity of technology. To solve this problem, there is a need to extract and utilize tacit knowledge and skills, but a method for utilizing disconnected information has been expected but not yet established. For instance, in the field of product failure, a method for appropriate integration of such fragmented information is expected to obtain systematic information about failure propagation. In this study, we obtain groups of failure cases with similar failure mechanisms from past automobile failure records (FPRs) by the following steps: 1. create a network among words such as part names or failure types (failure BOM), based on a single FPR; 2. create a network among FPR documents based on the common nodes of the failure BOM; 3. apply community detection algorithms to the network among FPR documents. As a result, comparing with the manually assigned labels about symptom and cause, the resulting community boundaries matches the label boundaries. Under the assumption that the failure BOM represents failure propagation, the proposed method can be evaluated as an FPR classification based on the likelihood of failure propagation. The proposed method is effective in indicating propagation-prone failure and in getting partial failure BOM based on failure propagation likelihood, which are expected to aid human analysis of faults.

Automatic Ignition Off For Bike When Brake Failure Using Limit Switch

We cannot predict the Actual tendency of an accident at what time that is occurs so it is always preferred to be cautious while designing a system that is related to utility of general public . brakes failure is one of the most commonly seen automobile failure where a person or a family tend to loose not just a man or a person but also their livelihood .The accident rate in India has been increasing day by day and human errors tend to occur as the nature of doing a thing by a human includes errors .mostly todays technologies has emerged to be completely automated but still when it comes to vehicles the brakes tend to be applied normally by a human. Particularly in relation to human life and health Some special safety systems have been designed into cars for the security of the passengers only, while others have been built for the safety of others. This is a brake disaster warning route that continuously tracks the dynamic state of the brake. If the brake fails, the switch activates, and the ignition turns off automatically.

Knowledge management of automobile system failures through development of failure knowledge ontology from maintenance experience

PurposeThe purpose of this paper is to develop an ontological model of failure knowledge of automobile systems that will enhance the knowledge management of automobile system failures, which will help for design and maintenance of automobiles. Failure knowledge of automobile systems and components gained through maintenance and repair can mitigate future failures, if integrated in the design. This is an outcome of this paper.Design/methodology/approachA failure coding scheme is developed for assimilating various entities of automobile failure knowledge and an ontological model is developed for its systematic structuring and representation. The developed failure code is a combination of alphanumeric and numeric code that incorporates ingredients of the failure knowledge, which will help database management, with reduced data entry time and storage space.FindingsThe maintenance of automobiles not only brings back the systems into operating conditions but also convey a lot of information regarding the failures. This is a useful input to the designers in development of reliable and maintainable automobile systems. A knowledge base can be created for automobile systems/components failures from their maintenance and service experience.Research limitations/implicationsDeveloped ontological model of automobile failure knowledge gained through maintenance experience can be shared across automobile manufacturers and service providers. This would help in design improvements, with ease and efficient undertaking of maintenance activities. This paper proposes the conceptual ontology structure, which is populated with three cases of automobile maintenance.Originality/valueThis research work is a first attempt to develop an ontological model for automobile failures from their maintenance and service experience. The novelty of the work is in its explicit consideration of all knowledge related to failures and maintenance of automobile systems, with their coding and structuring.

A bivariate model for analyzing recurrent multi-type automobile failures

The failure mechanism in an automobile can be defined as a system of multi-type recurrent failures where failures can occur due to various multi-type failure modes and these failures are repetitive such that more than one failure can occur from each failure mode. In analysing such automobile failures, both the time and type of the failure serve as response variables. However, these two response variables are highly correlated with each other since the timing of failures has an association with the mode of the failure. When there are more than one correlated response variables, the fitting of a multivariate model is more preferable than separate univariate models. Therefore, a bivariate model of time and type of failure becomes appealing for such automobile failure data. When there are multiple failure observations pertaining to a single automobile, such data cannot be treated as independent data because failure instances of a single automobile are correlated with each other while failures among different automobiles can be treated as independent. Therefore, this study proposes a bivariate model consisting time and type of failure as responses adjusted for correlated data. The proposed model was formulated following the approaches of shared parameter models and random effects models for joining the responses and for representing the correlated data respectively. The proposed model is applied to a sample of automobile failures with three types of failure modes and up to five failure recurrences. The parametric distributions that were suitable for the two responses of time to failure and type of failure were Weibull distribution and multinomial distribution respectively. The proposed bivariate model was programmed in SAS Procedure Proc NLMIXED by user programming appropriate likelihood functions. The performance of the bivariate model was compared with separate univariate models fitted for the two responses and it was identified that better performance is secured by the bivariate model. The proposed model can be used to determine the time and type of failure that would occur in the automobiles considered here.

Analysis on Automobile Engine Failure Diagnosis Based on Genetic Algorithm

The max stress is lying on the joint point of the carrier and planetary gear shaft because of the bending deformation of the shaft. The paper mainly introduces a method to analyze the automobile engine failure combining probability causal model and genetic algorithm. This method can quickly and accurately diagnose the complicated problems and troubles. And we have applied this kind of method into finding and diagnosing automobile engine failures and achieved a better results and effects. At the same time, it has also indicated that genetic algorithm works well in automobile failure diagnosis.

Research on the Vehicle Fault Diagnosis base on the Fuzzy Dependability Algorithm

This paper focuses on automobile failure detection and diagnostic accuracy, which have always been a hot research issue at home and abroad. Considering the deficiency of so many traditional fault diagnosis algorithms which failed to tell the complex relationship between the fault phenomenon and the reasons, and resulted in inaccurate fault diagnosis, this article proposed a new fault diagnosis of maximizing fuzzy dependability based on fuzzy rough set theory. This method could evaluate vehicle faults according to the dependency degree of condition attribute and calculate the probability of vehicle faults in line with fuzzy dependency degree. On this basis, the result will list out specific steps of fault diagnosis. Theoretical analysis and simulation experiments have proved the efficiency of this method on vehicle fault diagnosis. It could improve the reliability of fault monitoring.

An Assessment of RPN Prioritization in a Failure Modes Effects and Criticality Analysis

The Risk Priority Number methodology for prioritizing failure modes is an integral part of the Automobile Failure Modes and Effects Analysis (FMECA) technique. This technique consists of ranking potential failures from 1 to 10 with respect to their severity, probability of occurrence, and likelihood of detection in later tests, and multiplying the numbers. The result is a numerical ranking, called the RPN, on a scale from 1 to 1000. Potential failure modes having higher RPNs are assumed to have a higher design risk than those having lower values. Although this method is well documented and easy to apply, it is seriously flawed from a technical perspective, making the interpretation of the analysis results problematic. Problems with the methodology include: use of ordinal ranking numbers as numeric quantities; lack of continuity in the RPN measurement scale; duplicate RPN values with extremely different characteristics; and varying sensitivity to small changes. Recommendations for an improved methodology are provided.

Estimation from truncated lifetime data with supplementary information on covariates and censoring times

SUMMARY In epidemiology, reliability and other areas one encounters situations where response times and covariates for individuals in a population are observed only if the response time does not exceed an associated censoring time. Methods for obtaining supplementary information about covariates and censoring times for nonresponding individuals are considered. Estimation procedures are developed for the resulting combination of truncated data and supplementary data. Theoretical and simulation results are given, and the methodology illustrated on some automobile failure data collected under a warranty plan.

Estimation of Lifetime Parameters from Incomplete Field Data

When automobile failures occur within the automotive warranty period, a manufacturer can develop a record of mileages to failure from owners' requests for repair. When no failures occur during the warranty period the owner naturally will not report mileages, and it may be inferred that “no record of failures” means “no failures.” By using a follow-up survey or postal reply cards, data can be acquired to include a partial record of nonfailures. A method of estimating lifetime parameters is proposed for analyzing this kind of data. Assuming a Weibull and an exponential lifetime distribution under the random censoring model, I derive a consistent estimator that can be applied to any censoring distribution.

Estimation of Lifetime Parameters From Incomplete Field Data

When automobile failures occur within the automotive warranty period, a manufacturer can develop a record of mileages to failure from owners' requests for repair. When no failures occur during the warranty period the owner naturally will not report mileages, and it may be inferred that “no record of failures” means “no failures.” By using a follow-up survey or postal reply cards, data can be acquired to include a partial record of nonfailures. A method of estimating lifetime parameters is proposed for analyzing this kind of data. Assuming a Weibull and an exponential lifetime distribution under the random censoring model, I derive a consistent estimator that can be applied to any censoring distribution.