Predicting fault-prone software modules with rank sum classification

The detection and correction of defects remains among the most time consuming and expensive aspects of software development. Extensive automated testing and code inspections may mitigate their effect, but some code fragments are necessarily more likely to be faulty than others, and automated identification of fault prone modules helps to focus testing and inspections, thus limiting wasted effort and potentially improving detection rates. However, software metrics data is often extremely noisy, with enormous imbalances in the size of the positive and negative classes. In this work, we present a new approach to predictive modelling of fault proneness in software modules, introducing a new feature representation to overcome some of these issues. This rank sum representation offers improved or at worst comparable performance to earlier approaches for standard data sets, and readily allows the user to choose an appropriate trade-off between precision and recall to optimise inspection effort to suit different testing environments. The method is evaluated using the NASA Metrics Data Program (MDP) data sets, and performance is compared with existing studies based on the Support Vector Machine (SVM) and Naïve Bayes (NB) Classifiers, and with our own comprehensive evaluation of these methods.

Investigating the individual and organisational predictors of work-related driving crash involvement in Ethiopia

The rate of road traffic injury and death in Ethiopia is at a critical level when compared to rates in high-income countries. Considering the enormity of this issue, research is to identify groups of high-risk road users and the factors contributing to their crash involvement. This study focuses on work-related drivers. This study explores driving behaviour as a mediator of the relationship between organisational and individual attribute factors and self-reported crashes in a sample of 213 work-related drivers in Addis Ababa, Ethiopia. The hypothesised framework identifies driving behaviour as the most proximal determinant of self-reported crashes, and safety values, role overload and self-efficacy as antecedents of driving behaviour. With the exception of the relationship between self-efficacy and driving behaviour, all the hypothesised relationships were supported. We make recommendations for intervention approaches that are theoretically focused and sensitive to the cultural context.

Road crash risk after whiplash associated disorder

The future on-road safety of drivers affected by Whiplash Associated Disorder (WAD), the most common soft-tissue injury suffered in a traffic crash, has not been extensively explored. We obtained an anonymised file of 4280 insurance claimants with WAD and, as controls, 1116 claimants with comparably severe soft-tissue injuries who are considered to be at no increased risk than the general population. Their demographic information, road user type and traffic crash records both prior and subsequent to the traffic incident in which the injury occurred, the index crash, were obtained. Rates of subsequent crash involvement in these two groups were then compared, adjusting for age, sex, road user type and prior crash experience. The risk of a subsequent crash in the WAD group relative to controls was 1.14 (95% confidence interval, 0.87–1.48). To allow for differentially altered driving exposure after index crash we distributed a brief survey asking about changes in driving habits after a traffic crash involving injury via physiotherapy clinics and online through the electronic newsletter of a local motoring organisation. The survey yielded responses from 113 drivers who had experienced WAD in a traffic crash and 53 with other soft tissue injuries. There were no differences on average between the groups in their prior driving levels or their percentage change therein at one, three or six months after injury. There was thus no evidence that drivers with WAD are at any higher safety risk than drivers with other types of relatively minor post-crash soft tissue injury.

Acoustic emission condition monitoring : an application for wind turbine fault detection

Low speed rotating machines which are the most critical components in drive train of wind turbines are often menaced by several technical and environmental defects. These factors contribute to mount the economic requirement for Health Monitoring and Condition Monitoring of the systems. When a defect is happened in such system result in reduced energy loss rates from related process and due to it Condition Monitoring techniques that detecting energy loss are very difficult if not possible to use. However, in the case of Acoustic Emission (AE) technique this issue is partly overcome and is well suited for detecting very small energy release rates. Acoustic Emission (AE) as a technique is more than 50 years old and in this new technology the sounds associated with the failure of materials were detected. Acoustic wave is a non-stationary signal which can discover elastic stress waves in a failure component, capable of online monitoring, and is very sensitive to the fault diagnosis. In this paper the history and background of discovering and developing AE is discussed, different ages of developing AE which include Age of Enlightenment (1950-1967), Golden Age of AE (1967-1980), Period of Transition (1980-Present). In the next section the application of AE condition monitoring in machinery process and various systems that applied AE technique in their health monitoring is discussed. In the end an experimental result is proposed by QUT test rig which an outer race bearing fault was simulated to depict the sensitivity of AE for detecting incipient faults in low speed high frequency machine.

Application of machine learning technique in wind turbine fault diagnosis

In this study, a machine learning technique called anomaly detection is employed for wind turbine bearing fault detection. Basically, the anomaly detection algorithm is used to recognize the presence of unusual and potentially faulty data in a dataset, which contains two phases: a training phase and a testing phase. Two bearing datasets were used to validate the proposed technique, fault-seeded bearing from a test rig located at Case Western Reserve University to validate the accuracy of the anomaly detection method, and a test to failure data of bearings from the NSF I/UCR Center for Intelligent Maintenance Systems (IMS). The latter data set was used to compare anomaly detection with SVM, a previously well-known applied method, in rapidly finding the incipient faults.

Fault diagnosis of low speed bearing based on acoustic emission signal and multi-class relevance vector machine

This study presents an acoustic emission (AE) based fault diagnosis for low speed bearing using multi-class relevance vector machine (RVM). A low speed test rig was developed to simulate the various defects with shaft speeds as low as 10 rpm under several loading conditions. The data was acquired using anAEsensor with the test bearing operating at a constant loading (5 kN) andwith a speed range from20 to 80 rpm. This study is aimed at finding a reliable method/tool for low speed machines fault diagnosis based on AE signal. In the present study, component analysis was performed to extract the bearing feature and to reduce the dimensionality of original data feature. The result shows that multi-class RVM offers a promising approach for fault diagnosis of low speed machines.

Fault detection of slow speed rolling element bearing with noise removal techniques

Rolling Element Bearings (REBs) are vital components in rotating machineries for providing rotating motion. In slow speed rotating machines, bearings are normally subjected to heavy static loads and a catastrophic failure can cause enormous disruption to production and human safety. Due to its low operating speed the impact energy generated by the rotating elements on the defective components is not sufficient to produce a detectable vibration response. This is further aggravated by the inability of general measuring instruments to detect and process the weak signals at the initiation of the defect accurately. Furthermore, the weak signals are often corrupted by background noise. This is a serious problem faced by maintenance engineers today and the inability to detect an incipient failure of the machine can significantly increases the risk of functional failure and costly downtime. This paper presents the application of noise removal techniques for enhancing the detection capability for slow speed REB condition monitoring. Blind deconvolution (BD) and adaptive line enhancer (ALE) are compared to evaluate their performance in enhancing the source signal with consequential removal of background noise. In the experimental study, incipient defects were seeded on a number of roller bearings and the signals were acquired using acoustic emission (AE) sensor. Kurtosis and modified peak ratio (mPR) were used to determine the detectability of signal corrupted by noise.

Inclusion of tapered tubes in enhancing the crash performance of automotive frontal structures

This paper treats the design and analysis of an energy absorbing system. Experimental tests were conducted on a prototype, and these tests were used to validate a finite element model of the system. The model was then used to analyze the response of the system under dynamic impact loading. The response was compared with that of a similar system consisting of straight circular tubes, empty and foam-filled conical tubes. Three types of such supplementary devices were included in the energy absorbing system to examine the crush behavior and energy absorption capacity when subjected to axial and oblique impact loadings. The findings were used to develop design guidelines and recommendations for the implementation of tapered tubes in energy absorbing systems. To this end, the system was conceptual in form such that it could be adopted for a variety of applications. Nevertheless, for convenience, the approach in this study is to treat the system as a demonstrator car bumper system used to absorb impact energy during minor frontal collisions.

Sensor fault-resilient control of interior permanent-magnet synchronous motor drives

In a conventional ac motor drive using field-oriented control, a dc-link voltage, speed, and at least two current sensors are required. Hence, in the event of sensor failure, the performance of the drive system can be severely compromised. This paper presents a sensor fault-tolerant control strategy for interior permanent-magnet synchronous motor (IPMSM) drives. Three independent observers are proposed to estimate the speed, dc-link voltage, and currents of the machine. If a sensor fault is detected, the drive system isolates the faulty sensor while retaining the remaining functional ones. The signal is then acquired from the corresponding observer in order to maintain the operation of the drive system. The experimental results provided verify the effectiveness of the proposed approach.

Recursive actuator fault detection and diagnosis for emergency landing of UASs

This paper presents a practical recursive fault detection and diagnosis (FDD) scheme for online identification of actuator faults for unmanned aerial systems (UASs) based on the unscented Kalman filtering (UKF) method. The proposed FDD algorithm aims to monitor health status of actuators and provide indication of actuator faults with reliability, offering necessary information for the design of fault-tolerant flight control systems to compensate for side-effects and improve fail-safe capability when actuator faults occur. The fault detection is conducted by designing separate UKFs to detect aileron and elevator faults using a nonlinear six degree-of-freedom (DOF) UAS model. The fault diagnosis is achieved by isolating true faults by using the Bayesian Classifier (BC) method together with a decision criterion to avoid false alarms. High-fidelity simulations with and without measurement noise are conducted with practical constraints considered for typical actuator fault scenarios, and the proposed FDD exhibits consistent effectiveness in identifying occurrence of actuator faults, verifying its suitability for integration into the design of fault-tolerant flight control systems for emergency landing of UASs.

A sensor fault detection and isolation method in interior permanent-magnet synchronous motor drives based on an extended Kalman filter

Interior permanent-magnet synchronous motors (IPMSMs) become attractive candidates in modern hybrid electric vehicles and industrial applications. Usually, to obtain good control performance, the electric drives of this kind of motor require one position, one dc link, and at least two current sensors. Failure of any of these sensors might lead to degraded system performance or even instability. As such, sensor fault resilient control becomes a very important issue in modern drive systems. This paper proposes a novel sensor fault detection and isolation algorithm based on an extended Kalman filter. It is robust to system random noise and efficient in real-time implementation. Moreover, the proposed algorithm is compact and can detect and isolate all the sensor faults for IPMSM drives. Thorough theoretical analysis is provided, and the effectiveness of the proposed approach is proven by extensive experimental results.

Using police crash databases for injury prevention research - a comparison of opt-out and opt-in approaches to study recruitment

Objective: The study aimed to examine the difference in response rates between opt-out and opt-in participant recruitment in a population-based study of heavy-vehicle drivers involved in a police-attended crash. Methods: Two approaches to subject recruitment were implemented in two different states over a 14-week period and response rates for the two approaches (opt-out versus opt-in recruitment) were compared. Results: Based on the eligible and contactable drivers, the response rates were 54% for the optout group and 16% for the opt-in group. Conclusions and Implications: The opt-in recruitment strategy (which was a consequence of one jurisdiction’s interpretation of the national Privacy Act at the time) resulted in an insufficient and potentially biased sample for the purposes of conducting research into risk factors for heavy-vehicle crashes. Australia’s national Privacy Act 1988 has had a long history of inconsistent practices by state and territory government departments and ethical review committees. These inconsistencies can have profound effects on the validity of research, as shown through the significantly different response rates we reported in this study. It is hoped that a more unified interpretation of the Privacy Act across the states and territories, as proposed under the soon-to-be released Australian Privacy Principles will reduce the recruitment challenges outlined in this study.

Condition monitoring and fault diagnosis of diesel engines using instantaneous angular speed analysis

Continuous monitoring of diesel engine performance is critical for early detection of fault developments in an engine before they materialize into a functional failure. Instantaneous crank angular speed (IAS) analysis is one of a few nonintrusive condition monitoring techniques that can be utilized for such a task. Furthermore, the technique is more suitable for mass industry deployments than other non-intrusive methods such as vibration and acoustic emission techniques due to the low instrumentation cost, smaller data size and robust signal clarity since IAS is not affected by the engine operation noise and noise from the surrounding environment. A combination of IAS and order analysis was employed in this experimental study and the major order component of the IAS spectrum was used for engine loading estimation and fault diagnosis of a four-stroke four-cylinder diesel engine. It was shown that IAS analysis can provide useful information about engine speed variation caused by changing piston momentum and crankshaft acceleration during the engine combustion process. It was also found that the major order component of the IAS spectra directly associated with the engine firing frequency (at twice the mean shaft rotating speed) can be utilized to estimate engine loading condition regardless of whether the engine is operating at healthy condition or with faults. The amplitude of this order component follows a distinctive exponential curve as the loading condition changes. A mathematical relationship was then established in the paper to estimate the engine power output based on the amplitude of this order component of the IAS spectrum. It was further illustrated that IAS technique can be employed for the detection of a simulated exhaust valve fault in this study.

A gray-box neural network-based model identification and fault estimation scheme for nonlinear dynamic systems

A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.

Data mining the relationship between road crash and skid resistance

Road asset managers are seeking analysis of the whole road network to supplement statistical analyses of small subsets of homogeneous roadway. This study outlines the use of data mining capable of analyzing the wide range of situations found on the network, with a focus on the role of skid resistance in the cause of crashes. Results from the analyses show that on non-crash-prone roads with low crash rates, skid resistance contributes only in a minor way, whereas on high-crash roadways, skid resistance often contributes significantly in the calculation of the crash rate. The results provide evidence supporting a causal relationship between skid resistance and crashes and highlight the importance of the role of skid resistance in decision making in road asset management.