scholarly journals Spectrum-based Fault Localization Techniques Application on Multiple-Fault Programs: A Review

2020 ◽  
pp. 41-48
Author(s):  
Abubakar Zakari ◽  
Shamsu Abdullahi ◽  
Nura Modi Shagari ◽  
Abubakar Bello Tambawal ◽  
Nuruddeen Musa Shanono ◽  
...  
Keyword(s):  
Software Industry ◽  
Fault Localization ◽  
Research Trends ◽  
Fault Isolation ◽  
Multiple Fault ◽  
Multiple Faults ◽  
Program Debugging ◽  
High Utilization ◽  
Software Fault ◽  
Parallel Debugging

Software fault localization is one of the most tedious and costly activities in program debugging in the endeavor to identify faults locations in a software program. In this paper, the studies that used spectrum-based fault localization (SBFL) techniques that makes use of different multiple fault localization debugging methods such as one-bug-at-a-time (OBA) debugging, parallel debugging, and simultaneous debugging in localizing multiple faults are classified and critically analyzed in order to extensively discuss the current research trends, issues, and challenges in this field of study. The outcome strongly shows that there is a high utilization of OBA debugging method, poor fault isolation accuracy, and dominant use of artificial faults that limit the existing techniques applicability in the software industry.

Fault Localization Analysis for Multiple Fault Diagnosis in Multi-Station Assembly Systems

2007 ◽  
Author(s):  
Posheng Tsai ◽  
Kamal Mannar ◽  
Darek Ceglarek
Keyword(s):  
Manufacturing Systems ◽  
Large Scale ◽  
Fault Localization ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Isolation ◽  
Data Driven ◽  
Multiple Fault ◽  
Critical Measure ◽  
The Impact

Modern large scale multi-station manufacturing systems require effective variation reduction to improve the final assembly dimensional quality. One critical measure is to diagnose the fault in the process using knowledge-based root cause identification, which can be very challenging due to the complexity of the system. The paper investigates the need of data-driven fault localization to enhance the diagnosability within the context of multiple-fault scenario(s) in multi-station assembly processes where multivariate measurements are used. The paper proposes three types of fault-signal transmission in assembly system and illustrates the nature of structured noise. Moreover, the impact of structured noise on the diagnosability is illustrated on two major fault isolation methods, namely, Principal Component Analysis and Independent Component Analysis. We then propose to use data-driven fault localization to reduce the structured noise effect and enhance the diagnosability. A simulation case study based on automotive panel assembly model is provided to illustrate the impact of structured noise and the need for data-driven localization.

scholarly journals Research on Software Multiple Fault Localization Method Based on Machine Learning

2018 ◽  
Vol 232 ◽  
pp. 01060 ◽  
Author(s):  
Meng Gao ◽  
Pengyu Li ◽  
Congcong Chen ◽  
Yunsong Jiang
Keyword(s):  
Machine Learning ◽  
Fault Location ◽  
Fault Localization ◽  
Systematic Research ◽  
Multiple Fault ◽  
Multiple Faults ◽  
Machine Learning Classification ◽  
The Status ◽  
Coupling Fault ◽  
Program Spectrum

Fault localization is one of time-consuming and labor-intensive activity in the debugging process. Consequently, there is a strong demand for techniques that can guide software developers to the locations of faults in a program with high accuracy and minimal human intervention. Despite the research of neural network and decision tree has made some progress in software multiple fault localization, there is still a lack of systematic research on various algorithms of machine learning. Therefore, a novel machine-learning-based multiple faults localization is proposed in this paper. First, several concepts and connotation of software multiple fault localization are introduced, move on to the status and development trends of the research. Next, the principles of machine learning classification algorithm are explained. Then, a software multiple fault localization research framework based on machine learning is proposed. The process is taking the Mid function as an example, compares and analyzes the performance of 22 machine learning models in software multiple fault localization. Finally, the optimal machine learning method is verified in the multiple fault localization of the Siemens suite dataset. The experimental results show that the machine learning based on Random Forest algorithm has more accuracy and significant positioning efficiency. This paper effectively solved the problem of large amount of program spectrum data and multi-coupling fault location, which is very helpful for improving the efficiency of software multiple fault debugging.

Multiple faults detection and isolation approach applied to a kraft recovery boiler

TAPPI Journal ◽  
2014 ◽  
Vol 13 (1) ◽  
pp. 33-41
Author(s):  
YVON THARRAULT ◽  
MOULOUD AMAZOUZ
Keyword(s):  
Fault Detection ◽  
Early Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Principal Component ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Detection Scheme ◽  
Multiple Faults ◽  
Recovery Boiler

Recovery boilers play a key role in chemical pulp mills. Early detection of defects, such as water leaks, in a recovery boiler is critical to the prevention of explosions, which can occur when water reaches the molten smelt bed of the boiler. Early detection is difficult to achieve because of the complexity and the multitude of recovery boiler operating parameters. Multiple faults can occur in multiple components of the boiler simultaneously, and an efficient and robust fault isolation method is needed. In this paper, we present a new fault detection and isolation scheme for multiple faults. The proposed approach is based on principal component analysis (PCA), a popular fault detection technique. For fault detection, the Mahalanobis distance with an exponentially weighted moving average filter to reduce the false alarm rate is used. This filter is used to adapt the sensitivity of the fault detection scheme versus false alarm rate. For fault isolation, the reconstruction-based contribution is used. To avoid a combinatorial excess of faulty scenarios related to multiple faults, an iterative approach is used. This new method was validated using real data from a pulp and paper mill in Canada. The results demonstrate that the proposed method can effectively detect sensor faults and water leakage.

Fault Localization of Power-Ground Short via Signal Injection and Oscilloscope Technique

2010 ◽  
Author(s):  
Binh Nguyen
Keyword(s):  
Low Cost ◽  
Short Circuit ◽  
Fault Localization ◽  
Fault Isolation ◽  
Test Apparatus ◽  
Good Working ◽  
Signal Injection ◽  
Set Up ◽  
Non Destructive ◽  
Short Circuit Condition

Abstract For those attempting fault isolation on computer motherboard power-ground short issues, the optimal technique should utilize existing test equipment available in the debug facility, requiring no specialty equipment as well as needing a minimum of training to use effectively. The test apparatus should be both easy to set up and easy to use. This article describes the signal injection and oscilloscope technique which meets the above requirements. The signal injection and oscilloscope technique is based on the application of Ohm's law in a short-circuit condition. Two experiments were conducted to prove the effectiveness of these techniques. Both experiments simulate a short-circuit condition on the VCC3 power rail of a good working PC motherboard and then apply the signal injection and oscilloscope technique to localize the short. The technique described is a simple, low cost and non-destructive method that helps to find the location of the power-ground short quickly and effectively.

A Sample Preparation on Decapsulation Methodology for Effective Failure Analysis on Thin Small Leadless (TSLP) Flip Chip Package with Copper Pillar (CuP) Bump Interconnect Technology

2014 ◽  
Author(s):  
Gwee Hoon Yen ◽  
Ng Kiong Kay
Keyword(s):  
Sample Preparation ◽  
Failure Analysis ◽  
Flip Chip ◽  
Fault Localization ◽  
Cost Effective ◽  
Fault Isolation ◽  
Voltage Contrast ◽  
Interconnect Technology

Abstract Today, failure analysis involving flip chip [1] with copper pillar bump packaging technologies would be the major challenges faced by analysts. Most often, handling on the chips after destructive chemical decapsulation is extremely critical as there are several failure analysis steps to be continued such as chip level fault localization, chip micro probing for fault isolation, parallel lapping [2, 3, 4] and passive voltage contrast. Therefore, quality of sample preparation is critical. This paper discussed and demonstrated a quick, reliable and cost effective methodology to decapsulate the thin small leadless (TSLP) flip chip package with copper pillar (CuP) bump interconnect technology.

Magnetic Current Imaging Power Short Localization

2015 ◽  
Author(s):  
J. Gaudestad ◽  
F. Rusli ◽  
A. Orozco ◽  
M.C. Pun
Keyword(s):  
Thermal Imaging ◽  
Flip Chip ◽  
Optical Microscope ◽  
Fault Isolation ◽  
Magnetic Current ◽  
Multiple Fault ◽  
Defect Location ◽  
Isolation Techniques ◽  
Chip Sample

Abstract A Flip Chip sample failed short between power and ground. The reference unit had 418Ω and the failed unit with the short had 16.4Ω. Multiple fault isolation techniques were used in an attempt to find the failure with thermal imaging and Magnetic Current Imaging being the only techniques capable of localizing the defect. To physically verify the defect location, the die was detached from the substrate and a die cracked was seen using a visible optical microscope.

Sign in / Sign up

Export Citation Format

Share Document