Motor current signal analysis using a modified bispectrum for machine fault diagnosis

This paper presents the use of the induction motor current to identify and quantify common faults within a two-stage reciprocating compressor. The theoretical basis is studied to understand current signal characteristics when the motor undertakes a varying load under faulty conditions. Although conventional bispectrum representation of current signal allows the inclusion of phase information and the elimination of Gaussian noise, it produces unstable results due to random phase variation of the sideband components in the current signal. A modified bispectrum based on the amplitude modulation feature of the current signal is thus proposed to combine both lower sidebands and higher sidebands simultaneously and hence describe the current signal more accurately. Based on this new bispectrum a more effective diagnostic feature namely normalised bispectral peak is developed for fault classification. In association with the kurtosis of the raw current signal, the bispectrum feature gives rise to reliable fault classification results. In particular, the low feature values can differentiate the belt looseness from other fault cases and discharge valve leakage and intercooler leakage can be separated easily using two linear classifiers. This work provides a novel approach to the analysis of stator current for the diagnosis of motor drive faults from downstream driving equipment

Diversity with AntiVirus products: Additional empirical studies

In this paper we describe the design of a new set of empirical studies we will run to test the gains in detection capabilities from using diverse AntiVirus products. This new work builds on previous work on this topic reported in [1, 2, 3]. We describe the motivation for this work, how it extends the previous work and what studies we will conduct

Automated Synthesis of SEU Tolerant Architectures from OO Descriptions

SEU faults are a well-known problem in aerospace environment but recently their relevance grew up also at ground level in commodity applications coupled, in this frame, with strong economic constraints in terms of costs reduction. On the other hand, latest hardware description languages and synthesis tools allow reducing the boundary between software and hardware domains making the high-level descriptions of hardware components very similar to software programs. Moving from these considerations, the present paper analyses the possibility of reusing Software Implemented Hardware Fault Tolerance (SIHFT) techniques, typically exploited in micro-processor based systems, to design SEU tolerant architectures. The main characteristics of SIHFT techniques have been examined as well as how they have to be modified to be compatible with the synthesis flow. A complete environment is provided to automate the design instrumentation using the proposed techniques, and to perform fault injection experiments both at behavioural and gate level. Preliminary results presented in this paper show the effectiveness of the approach in terms of reliability improvement and reduced design effort

An Experimental Study of Diversity with Off-The-Shelf AntiVirus Engines

Fault tolerance in the form of diverse redundancy is well known to improve the detection rates for both malicious and non-malicious failures. What is of interest to designers of security protection systems are the actual gains in detection rates that they may give. In this paper we provide exploratory analysis of the potential gains in detection capability from using diverse AntiVirus products for the detection of self-propagating malware. The analysis is based on 1599 malware samples collected by the operation of a distributed honeypot deployment over a period of 178 days. We sent these samples to the signature engines of 32 different AntiVirus products taking advantage of the VirusTotal service. The resulting dataset allowed us to perform analysis of the effects of diversity on the detection capability of these components as well as how their detection capability evolves in time

Fault detection of helicopter gearboxes using the multi-valued influence matrix method

In this paper we investigate the effectiveness of a pattern classifying fault detection system that is designed to cope with the variability of fault signatures inherent in helicopter gearboxes. For detection, the measurements are monitored on-line and flagged upon the detection of abnormalities, so that they can be attributed to a faulty or normal case. As such, the detection system is composed of two components, a quantization matrix to flag the measurements, and a multi-valued influence matrix (MVIM) that represents the behavior of measurements during normal operation and at fault instances. Both the quantization matrix and influence matrix are tuned during a training session so as to minimize the error in detection. To demonstrate the effectiveness of this detection system, it was applied to vibration measurements collected from a helicopter gearbox during normal operation and at various fault instances. The results indicate that the MVIM method provides excellent results when the full range of faults effects on the measurements are included in the training set

Event Analysis of Pulse-reclosers in Distribution Systems Through Sparse Representation

The pulse-recloser uses pulse testing technology to verify that the line is clear of faults before initiating a reclose operation, which significantly reduces stress on the system components (e.g. substation transformers) and voltage sags on adjacent feeders. Online event analysis of pulse-reclosers are essential to increases the overall utility of the devices, especially when there are numerous devices installed throughout the distribution system. In this paper, field data recorded from several devices were analyzed to identify specific activity and fault locations. An algorithm is developed to screen the data to identify the status of each pole and to tag time windows with a possible pulse event. In the next step, selected time windows are further analyzed and classified using a sparse representation technique by solving an l1-regularized least-square problem. This classification is obtained by comparing the pulse signature with the reference dictionary to find a set that most closely matches the pulse features. This work also sheds additional light on the possibility of fault classification based on the pulse signature. Field data collected from a distribution system are used to verify the effectiveness and reliability of the proposed method.Comment: Accepted in: 19th International Conference on Intelligent System Application to Power Systems (ISAP), San Antonio, TX, 201

The Immune System: the ultimate fractionated cyber-physical system

In this little vision paper we analyze the human immune system from a computer science point of view with the aim of understanding the architecture and features that allow robust, effective behavior to emerge from local sensing and actions. We then recall the notion of fractionated cyber-physical systems, and compare and contrast this to the immune system. We conclude with some challenges.Comment: In Proceedings Festschrift for Dave Schmidt, arXiv:1309.455