Effectiveness of MED for Fault Diagnosis in Roller Bearings

Diagnostics of rolling element bearings is usually performed by means of vibration signals measured by accelerometers placed in the proximity of the bearing under investigation. The aim is to monitor the integrity of the bearing components, in order to avoid catastrophic failures, or to implement condition based maintenance strategies. In particular, the trend in this field is to combine in a single algorithm different signal-enhancement and signal-analysis techniques. Among the first ones, Minimum Entropy Deconvolution (MED) has been pointed out as a key tool able to highlight the effect of a possible damage in one of the bearing components within the vibration signal. This paper presents the application of this technique to signals collected on a simple test-rig, able to test damaged industrial roller bearings in different working conditions. The effectiveness of the technique has been tested, comparing the results of one undamaged bearing with three bearings artificially damaged in different locations, namely on the inner race, outer race and rollers. Since MED performances are dependent on the filter length, the most suitable value of this parameter is defined on the basis of both the application and measured signals. This represents an original contribution of the paper.

Bearing Fault Diagnostics Using the Spectral Pattern Recognition

In the field of diagnostics of rolling element bearings, the development of sophisticated techniques, such as Spectral Kurtosis and 2nd Order Cyclostationarity, extended the capability of expert users to identify not only the presence, but also the location of the damage in the bearing. Most of the signal-analysis methods, as the ones previously mentioned, result in a spectrum-like diagram that presents line frequencies or peaks in the neighbourhood of some theoretical characteristic frequencies, in case of damage. These frequencies depend only on damage position, bearing geometry and rotational speed. The major improvement in this field would be the development of algorithms with high degree of automation. This paper aims at this important objective, by discussing for the first time how these peaks can draw away from the theoretical expected frequencies as a function of different working conditions, i.e. speed, torque and lubrication. After providing a brief description of the peak-patterns associated with each type of damage, this paper shows the typical magnitudes of the deviations from the theoretical expected frequencies. The last part of the study presents some remarks about increasing the reliability of the automatic algorithm. The research is based on experimental data obtained by using artificially damaged bearings installed in a gearbox.

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.

Continuous disclosure compliance: Does corporate governance matter?

Our study investigates the quality of firms’ continuous disclosure compliance during mandatory continuous disclosure reform, and whether the compliance quality is impacted by corporate governance, using the New Zealand market as the setting. We use a novel coding of different categories of disclosures (nonroutine, non-procedural and internal), which represents the extent of proprietary insider information inherent in disclosures, to evaluate firms’compliance quality. Our findings provide evidence that firms’ compliance quality improved after the reform, and this improvement is inconsistently impacted by corporate gvernance. Our findings provide important implications for regulators in their quest for a superior disclosure regime

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.

Active awareness : supporting the intentional disclosure of awareness information in collaborative systems

This thesis opens up the design space for awareness research in CSCW and HCI. By challenging the prevalent understanding of roles in awareness processes and exploring different mechanisms for actively engaging users in the awareness process, this thesis provides a better understanding of the complexity of these processes and suggests practical solutions for designing and implementing systems that support active awareness. Mutual awareness, a prominent research topic in the fields of Computer-Supported Cooperative Work (CSCW) and Human-Computer Interaction (HCI) refers to a fundamental aspect of a person’s work: their ability to gain a better understanding of a situation by perceiving and interpreting their co-workers actions. Technologically-mediated awareness, used to support co-workers across distributed settings, distinguishes between the roles of the actor, whose actions are often limited to being the target of an automated data gathering processes, and the receiver, who wants to be made aware of the actors’ actions. This receiver-centric view of awareness, focusing on helping receivers to deal with complex sets of awareness information, stands in stark contrast to our understanding of awareness as social process involving complex interactions between both actors and receivers. It fails to take into account an actors’ intimate understanding of their own activities and the contribution that this subjective understanding could make in providing richer awareness information. In this thesis I challenge the prevalent receiver-centric notion of awareness, and explore the conceptual foundations, design, implementation and evaluation of an alternative active awareness approach by making the following five contributions. Firstly, I identify the limitations of existing awareness research and solicit further evidence to support the notion of active awareness. I analyse ethnographic workplace studies that demonstrate how actors engage in an intricate interplay involving the monitoring of their co-workers progress and displaying aspects of their activities that may be of relevance to others. The examination of a large body of awareness research reveals that while disclosing information is a common practice in face-to-face collaborative settings it has been neglected in implementations of technically mediated awareness. Based on these considerations, I introduce the notion of intentional disclosure to describe the action of users actively and deliberately contributing awareness information. I consider challenges and potential solutions for the design of active awareness. I compare a range of systems, each allowing users to share information about their activities at various levels of detail. I discuss one of the main challenges to active awareness: that disclosing information about activities requires some degree of effort. I discuss various representations of effort in collaborative work. These considerations reveal that there is a trade-off between the richness of awareness information and the effort required to provide this information. I propose a framework for active awareness, aimed to help designers to understand the scope and limitations of different types of intentional disclosure. I draw on the identified richness/effort trade-off to develop two types of intentional disclosure, both of which aim to facilitate the disclosure of information while reducing the effort required to do so. For both of these approaches, direct and indirect disclosure, I delineate how they differ from related approaches and define a set of design criteria that is intended to guide their implementation. I demonstrate how the framework of active awareness can be practically applied by building two proof-of-concept prototypes that implement direct and indirect disclosure respectively. AnyBiff, implementing direct disclosure, allows users to create, share and use shared representations of activities in order to express their current actions and intentions. SphereX, implementing indirect disclosure, represents shared areas of interests or working context, and links sets of activities to these representations. Lastly, I present the results of the qualitative evaluation of the two prototypes and analyse the results with regard to the extent to which they implemented their respective disclosure mechanisms and supported active awareness. Both systems were deployed and tested in real world environments. The results for AnyBiff showed that users developed a wide range of activity representations, some unanticipated, and actively used the system to disclose information. The results further highlighted a number of design considerations relating to the relationship between awareness and communication, and the role of ambiguity. The evaluation of SphereX validated the feasibility of the indirect disclosure approach. However, the study highlighted the challenges of implementing cross-application awareness support and translating the concept to users. The study resulted in design recommendations aimed to improve the implementation of future systems.

Court stands firm on disclosure via subpoena

In Elders Rural Services Australia Ltd v Gooden [2014] QDC 22 Reid DCJ considered the interaction of the procedures under the Uniform Civil Procedure Rules 1999 (Qld)relating to disclosure by parties to a proceeding and the subpoena process, in the context of a proceeding commenced by originating application.

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.

Disclosure of climate change-related corporate governance practices

Based on a survey of climate change experts in different stakeholder groups and interviews with corporate climate change managers, this study provides insights into the gap between what information stakeholders expect and what Australian corporations disclose. This paper focuses on annual reports and sustainability reports with specific reference to the disclosure of climate change-related corporate governance practices. The findings culminate in the refinement of a best practice index for the disclosure of climate-change-related corporate governance practises. Interview results indicate that the low levels of disclosures made by Australian companies may be due to a number of factors. These include a potential expectations gap, the absence of pressure from powerful stakeholders, a concern for stakeholder information overload, the cost of providing information, limited perceived accountability for climate change, and preferring other media for disclosure.

Disclosure obligations - Personal Injuries Proceedings Act 2002

In Huag v Jupiters Limited [2007] QSC 068, Lyons J considered the extent of the obligations imposed upon a respondent under the Personal Injuries Proceedings Act 2002 to disclose documents and information.

Practice & procedure : disclosure obligations - Personal Injuries Proceedings Act 2002 s27

In Huag v Jupiters Limited [2007] QCA 199 the Queensland Court of Appeal allowed an appeal from interlocutory orders made in the trial division of the court and concluded that, although provisions such as s27 of the Personal Injuries Proceedings Act 2002 (Qld) should be given a broad, remedial construction, this did not mean the words of limitation in the section could be ignored.

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.