Utilising Reliability and Condition Monitoring Data for Asset Health Prognosis

The ability to forecast machinery health is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models which attempt to forecast machinery health based on condition data such as vibration measurements. This paper demonstrates how the population characteristics and condition monitoring data (both complete and suspended) of historical items can be integrated for training an intelligent agent to predict asset health multiple steps ahead. The model consists of a feed-forward neural network whose training targets are asset survival probabilities estimated using a variation of the Kaplan–Meier estimator and a degradation-based failure probability density function estimator. The trained network is capable of estimating the future survival probabilities when a series of asset condition readings are inputted. The output survival probabilities collectively form an estimated survival curve. Pump data from a pulp and paper mill were used for model validation and comparison. The results indicate that the proposed model can predict more accurately as well as further ahead than similar models which neglect population characteristics and suspended data. This work presents a compelling concept for longer-range fault prognosis utilising available information more fully and accurately.

Underground power cable environment on-line monitoring and analysis

Knowledge of cable parameters has been well established but a better knowledge of the environment in which the cables are buried lags behind. Research in Queensland University of Technology has been aimed at obtaining and analysing actual daily field values of thermal resistivity and diffusivity of the soil around power cables. On-line monitoring systems have been developed and installed with a data logger system and buried spheres that use an improved technique to measure thermal resistivity and diffusivity over a short period. Results based on long term continuous field data are given. A probabilistic approach is developed to establish the correlation between the measured field thermal resistivity values and rainfall data from weather bureau records. This data from field studies can reduce the risk in cable rating decisions and provide a basis for reliable prediction of “hot spot” of an existing cable circuit

Monitoring smartphones for anomaly detection

In this paper we demonstrate how to monitor a smartphone running Symbian operating system and Windows Mobile in order to extract features for anomaly detection. These features are sent to a remote server because running a complex intrusion detection system on this kind of mobile device still is not feasible due to capability and hardware limitations. We give examples on how to compute relevant features and introduce the top ten applications used by mobile phone users based on a study in 2005. The usage of these applications is recorded by a monitoring client and visualized. Additionally, monitoring results of public and self-written malwares are shown. For improving monitoring client performance, Principal Component Analysis was applied which lead to a decrease of about 80 of the amount of monitored features.

Towards persistent indoor appearance-based localization, mapping and navigation using CAT-Graph

The challenge of persistent appearance-based navigation and mapping is to develop an autonomous robotic vision system that can simultaneously localize, map and navigate over the lifetime of the robot. However, the computation time and memory requirements of current appearance-based methods typically scale not only with the size of the environment but also with the operation time of the platform; also, repeated revisits to locations will develop multiple competing representations which reduce recall performance. In this paper we present a solution to the persistent localization, mapping and global path planning problem in the context of a delivery robot in an office environment over a one-week period. Using a graphical appearance-based SLAM algorithm, CAT-Graph, we demonstrate constant time and memory loop closure detection with minimal degradation during repeated revisits to locations, along with topological path planning that improves over time without using a global metric representation. We compare the localization performance of CAT-Graph to openFABMAP, an appearance-only SLAM algorithm, and the path planning performance to occupancy-grid based metric SLAM. We discuss the limitations of the algorithm with regard to environment change over time and illustrate how the topological graph representation can be coupled with local movement behaviors for persistent autonomous robot navigation.

Temporal trend of organochlorine pesticides in Australia

Persistent, lipophilic organochlorine pesticides (OCPs) such as dichlorodiphenyltrichloroethane (DDT), hexachlorocyclohexanes (HCHs), dieldrin, chlordanes, hexachlorobenzene (HCB) and mirex are known to accumulate in human samples [1, 2]. Persistent OCPs are among the chemicals that are covered under the Stockholm Convention on persistent organic pollutants [3]. Exceptions to this include relatively less lipophillic compounds like HCH (KOW<10^5). In Australia, OCPs such as DDT and HCHs were introduced in the 1940s. This followed a period of widespread use until the 1970s when recognition of risks related to OCPs resulted in reduced use and their ultimate ban in the 1980s. Mirex, however, remained in very restricted use in Northern Australia for treatment of one species of termites (the Giant Termite (Mastotermes darwinensis)) but this use was phased out in 2007.

Using a pharmacokinetic model to interpret biomonitoring data of PBDEs in the Australian population

From human biomonitoring data that are increasingly collected in the United States, Australia, and in other countries from large-scale field studies, we obtain snap-shots of concentration levels of various persistent organic pollutants (POPs) within a cross section of the population at different times. Not only can we observe the trends within this population with time, but we can also gain information going beyond the obvious time trends. By combining the biomonitoring data with pharmacokinetic modeling, we can re-construct the time-variant exposure to individual POPs, determine their intrinsic elimination half-lives in the human body, and predict future levels of POPs in the population. Different approaches have been employed to extract information from human biomonitoring data. Pharmacokinetic (PK) models were combined with longitudinal data1, with single2 or multiple3 average concentrations of a cross-sectional data (CSD), or finally with multiple CSD with or without empirical exposure data4. In the latter study, for the first time, the authors based their modeling outputs on two sets of CSD and empirical exposure data, which made it possible that their model outputs were further constrained due to the extensive body of empirical measurements. Here we use a PK model to analyze recent levels of PBDE concentrations measured in the Australian population. In this study, we are able to base our model results on four sets5-7 of CSD; we focus on two PBDE congeners that have been shown3,5,8-9 to differ in intake rates and half-lives with BDE-47 being associated with high intake rates and a short half-life and BDE-153 with lower intake rates and a longer half-life. By fitting the model to PBDE levels measured in different age groups in different years, we determine the level of intake of BDE-47 and BDE-153, as well as the half-lives of these two chemicals in the Australian population.

Behavioral biometrics for persistent single sign-on

Driven by the rapid development of ubiquitous and pervasive computing, personalized services and applications are deployed to support our lives. Accordingly, the number of interfaces and devices (smartphone, tablet computer, etc.) provided to access and consume these services is growing continuously. To simplify the complexity of managing many accounts with different credentials, Single Sign-On (SSO) solutions have been introduced. However, a single password for many accounts represents a single-point-of-failure. Furthermore, once initiated SSO session is a high potential risk when the working station is left unlocked and unattended. In this paper, we present a conception of a Persistent Single Sign-On (PSSO) for ubiquitous home environments by involving the capabilities of Behavioral Biometrics to check the identity of the user continuously in an unobtrusive manner.

Business process management : an introduction to the special focus issue

The management and improvement of business processes are a core topic of the information systems discipline. The persistent demand in corporations within all industry sectors for increased operational efficiency and innovation, an emerging set of established and evaluated methods, tools, and techniques as well as the quickly growing body of academic and professional knowledge are indicative for the standing that Business Process Management (BPM) has nowadays. During the last decades, intensive research has been conducted with respect to the design, implementation, execution, and monitoring of business processes. Comparatively low attention, however, has been paid to questions related to organizational issues such as the adoption, usage, implications, and overall success of BPM approaches, technologies, and initiatives. This research gap motivated us to edit a corresponding special focus issue for the journal BISE/WIRTSCHAFTSINFORMATIK. We are happy that we are able to present a selection of three research papers and a state-of-the-art paper in the scientific section of the issue at hand. As these papers differ in the topics they investigate, the research method they apply, and the theoretical foundations they build on, the diversity within the BPM field becomes evident. The academic papers are complemented by an interview with Phil Gilbert, IBM’s Vice President for Business Process and Decision Management, who reflects on the relationship between business processes and the data flowing through them, the need to establish a process context for decision making, and the calibration of BPM efforts toward executives who see processes as a means to an end, rather than a first-order concept in its own right.

Real-time risk monitoring in business processes : a sensor-based approach

This article proposes an approach for real-time monitoring of risks in executable business process models. The approach considers risks in all phases of the business process management lifecycle, from process design, where risks are defined on top of process models, through to process diagnosis, where risks are detected during process execution. The approach has been realized via a distributed, sensor-based architecture. At design-time, sensors are defined to specify risk conditions which when fulfilled, are a likely indicator of negative process states (faults) to eventuate. Both historical and current process execution data can be used to compose such conditions. At run-time, each sensor independently notifies a sensor manager when a risk is detected. In turn, the sensor manager interacts with the monitoring component of a business process management system to prompt the results to process administrators who may take remedial actions. The proposed architecture has been implemented on top of the YAWL system, and evaluated through performance measurements and usability tests with students. The results show that risk conditions can be computed efficiently and that the approach is perceived as useful by the participants in the tests.

Performance characterization of VGCF/epoxy nanocomposite sensors under static load cycles and in static structural health monitoring

Compared to conventional metal-foil strain gauges, nanocomposite piezoresistive strain sensors have demonstrated high strain sensitivity and have been attracting increasing attention in recent years. To fulfil their ultimate success, the performance of vapor growth carbon fiber (VGCF)/epoxy nanocomposite strain sensors subjected to static cyclic loads was evaluated in this work. A strain-equivalent quantity (resistance change ratio) in cantilever beams with intentionally induced notches in bending was evaluated using the conventional metal-foil strain gauges and the VGCF/epoxy nanocomposite sensors. Compared to the metal-foil strain gauges, the nanocomposite sensors are much more sensitive to even slight structural damage. Therefore, it was confirmed that the signal stability, reproducibility, and durability of these nanocomposite sensors are very promising, leading to the present endeavor to apply them for static structural health monitoring.

Monitoring android for collaborative anomaly detection : a first architectural draft

Our daily lives become more and more dependent upon smartphones due to their increased capabilities. Smartphones are used in various ways, e.g. for payment systems or assisting the lives of elderly or disabled people. Security threats for these devices become more and more dangerous since there is still a lack of proper security tools for protection. Android emerges as an open smartphone platform which allows modification even on operating system level and where third-party developers first time have the opportunity to develop kernel-based low-level security tools. Android quickly gained its popularity among smartphone developers and even beyond since it bases on Java on top of "open" Linux in comparison to former proprietary platforms which have very restrictive SDKs and corresponding APIs. Symbian OS, holding the greatest market share among all smartphone OSs, was even closing critical APIs to common developers and introduced application certification. This was done since this OS was the main target for smartphone malwares in the past. In fact, more than 290 malwares designed for Symbian OS appeared from July 2004 to July 2008. Android, in turn, promises to be completely open source. Together with the Linux-based smartphone OS OpenMoko, open smartphone platforms may attract malware writers for creating malicious applications endangering the critical smartphone applications and owners privacy. Since signature-based approaches mainly detect known malwares, anomaly-based approaches can be a valuable addition to these systems. They base on mathematical algorithms processing data that describe the state of a certain device. For gaining this data, a monitoring client is needed that has to extract usable information (features) from the monitored system. Our approach follows a dual system for analyzing these features. On the one hand, functionality for on-device light-weight detection is provided. But since most algorithms are resource exhaustive, remote feature analysis is provided on the other hand. Having this dual system enables event-based detection that can react to the current detection need. In our ongoing research we aim to investigates the feasibility of light-weight on-device detection for certain occasions. On other occasions, whenever significant changes are detected on the device, the system can trigger remote detection with heavy-weight algorithms for better detection results. In the absence of the server respectively as a supplementary approach, we also consider a collaborative scenario. Here, mobile devices sharing a common objective are enabled by a collaboration module to share information, such as intrusion detection data and results. This is based on an ad-hoc network mode that can be provided by a WiFi or Bluetooth adapter nearly every smartphone possesses.

Social signal processing for pain monitoring using a hidden conditional random field

utomatic pain monitoring has the potential to greatly improve patient diagnosis and outcomes by providing a continuous objective measure. One of the most promising methods is to do this via automatically detecting facial expressions. However, current approaches have failed due to their inability to: 1) integrate the rigid and non-rigid head motion into a single feature representation, and 2) incorporate the salient temporal patterns into the classification stage. In this paper, we tackle the first problem by developing a “histogram of facial action units” representation using Active Appearance Model (AAM) face features, and then utilize a Hidden Conditional Random Field (HCRF) to overcome the second issue. We show that both of these methods improve the performance on the task of pain detection in sequence level compared to current state-of-the-art-methods on the UNBC-McMaster Shoulder Pain Archive.

A wide area monitoring system based load restoration method

Restoring a large-scale power system has always been a complicated and important issue. A lot of research work has been done on different aspects of the whole power system restoration procedure. However, more time will be required to complete the power system restoration process in an actual situation if accurate and real-time system data cannot be obtained. With the development of the wide area monitoring system (WAMS), power system operators are capable of accessing to more accurate data in the restoration stage after a major outage. The ultimate goal of the system restoration is to restore as much load as possible while in the shortest period of time after a blackout, and the restorable load can be estimated by employing WAMS. Moreover, discrete restorable loads are employed considering the limited number of circuit-breaker operations and the practical topology of distribution systems. In this work, a restorable load estimation method is proposed employing WAMS data after the network frame has been reenergized, and WAMS is also employed to monitor the system parameters in case the newly recovered system becomes unstable again. The proposed method has been validated with the New England 39-Bus system and an actual power system in Guangzhou, China.

Risk modelling in quality clinical registries : Monitoring lesion treatment failure rate in percutaneous coronary interventions

Aims: This paper describes the development of a risk adjustment (RA) model predictive of individual lesion treatment failure in percutaneous coronary interventions (PCI) for use in a quality monitoring and improvement program. Methods and results: Prospectively collected data for 3972 consecutive revascularisation procedures (5601 lesions) performed between January 2003 and September 2011 were studied. Data on procedures to September 2009 (n = 3100) were used to identify factors predictive of lesion treatment failure. Factors identified included lesion risk class (p < 0.001), occlusion type (p < 0.001), patient age (p = 0.001), vessel system (p < 0.04), vessel diameter (p < 0.001), unstable angina (p = 0.003) and presence of major cardiac risk factors (p = 0.01). A Bayesian RA model was built using these factors with predictive performance of the model tested on the remaining procedures (area under the receiver operating curve: 0.765, Hosmer–Lemeshow p value: 0.11). Cumulative sum, exponentially weighted moving average and funnel plots were constructed using the RA model and subjectively evaluated. Conclusion: A RA model was developed and applied to SPC monitoring for lesion failure in a PCI database. If linked to appropriate quality improvement governance response protocols, SPC using this RA tool might improve quality control and risk management by identifying variation in performance based on a comparison of observed and expected outcomes.

A Systematic review of a statistical vacuum circuit breaker model for condition monitoring

Vacuum circuit breaker (VCB) overvoltage failure and its catastrophic failures during shunt reactor switching have been analyzed through computer simulations for multiple reignitions with a statistical VCB model found in the literature. However, a systematic review (SR) that is related to the multiple reignitions with a statistical VCB model does not yet exist. Therefore, this paper aims to analyze and explore the multiple reignitions with a statistical VCB model. It examines the salient points, research gaps and limitations of the multiple reignition phenomenon to assist with future investigations following the SR search. Based on the SR results, seven issues and two approaches to enhance the current statistical VCB model are identified. These results will be useful as an input to improve the computer modeling accuracy as well as the development of a reignition switch model with point-on-wave controlled switching for condition monitoring