Sensorless fault diagnosis of centrifugal pumps

Analysis of electrical signatures has been in use for some time to assess the condition of induction motors. In most applications, induction motors are used to drive dynamic loads, such as pumps, fans, and blowers, by means of belts, couplers and gear-boxes. Failure of either the electric motors or the driven loads is associated with operational disruptions. The large costs associated with the resulting idle equipment and personnel can often be avoided if the degradation is detected in its early stages, prior to reaching catastrophic failure conditions. Hence the need arises for cost- effective schemes to assess not only the condition of the motor but also of the driven load. This work presents an experimentally demonstrated sensorless approach for model- based detection of three different classes of faults that frequently occur in centrifugal pumps. A fault isolation scheme is also developed to distinguish between motor re- lated and pump related faults. The proposed approach is sensorless, in the sense that no mechanical sensors are required on either the pump or the motor driving the pump. Rather, fault detection and isolation is carried out using only the line voltages and phase currents of the electric motor driving the pump, as measured through standard potential transformers (PT's) and current transformers (CT's) found in industrial switchgear. The developed fault detection and isolation scheme is insensitive to electric power supply variations. Furthermore, it does not require a priori knowledge of a motor or pump model or any detailed motor or pump design parameters; a model of the system is adaptively estimated on-line. The developed algorithms have been tested on three types of staged pump faults using data collected from a centrifugal pump connected to a 3, 3 hp induction motor. Results from these experiments indicate that the proposed model-based detection scheme effectively detects all staged faults with fault detection times comparable to those obtained from vibration analysis. In addition to the staged fault experiments, extended healthy operation reveals no false alarms by the proposed detection algorithm. The proposed fault isolation method successfully classifies faults in the motor and the pump without any mis-classification

Fault-tolerant multilevel converter to feed a switched reluctance machine

The switched reluctance machine (SRM) is one of the most interesting machines, being adopted for many applications. However, this machine requires a power electronic converter that usually is the most fragile element of the system. Thus, in order to ensure high reliability for this system, it is fundamental to design a power electronic converter with fault-tolerant capability. In this context, a new solution is proposed to give this capability to the system. This converter was designed with the purpose to ensure fault-tolerant capability to two types of switch faults, namely open- and short-circuit. Moreover, apart from this feature, the proposed topology is characterized by a multilevel operation that allows improvement of the performance of the SRM, taking into consideration a wide speed range. Although the proposed solution is presented for an 8/6 SRM, it can be used for other configurations. The operation of the proposed topology will be described for the two modes, fault-tolerant and normal operation. Another aspect that is addressed in this paper is the proposal of fault detection and diagnosis method for this fault-tolerant inverter. It was specifically developed for a multilevel SRM drive. The theoretical assumptions will be verified through two different types of tests, firstly by simulation and secondly by experiments with a laboratory prototype.info:eu-repo/semantics/publishedVersio

A Machine-learning Based Ensemble Method For Anti-patterns Detection

Anti-patterns are poor solutions to recurring design problems. Several empirical studies have highlighted their negative impact on program comprehension, maintainability, as well as fault-proneness. A variety of detection approaches have been proposed to identify their occurrences in source code. However, these approaches can identify only a subset of the occurrences and report large numbers of false positives and misses. Furthermore, a low agreement is generally observed among different approaches. Recent studies have shown the potential of machine-learning models to improve this situation. However, such algorithms require large sets of manually-produced training-data, which often limits their application in practice. In this paper, we present SMAD (SMart Aggregation of Anti-patterns Detectors), a machine-learning based ensemble method to aggregate various anti-patterns detection approaches on the basis of their internal detection rules. Thus, our method uses several detection tools to produce an improved prediction from a reasonable number of training examples. We implemented SMAD for the detection of two well known anti-patterns: God Class and Feature Envy. With the results of our experiments conducted on eight java projects, we show that: (1) our method clearly improves the so aggregated tools; (2) SMAD significantly outperforms other ensemble methods.Comment: Preprint Submitted to Journal of Systems and Software, Elsevie

Kernel Ellipsoidal Trimming

Ellipsoid estimation is an issue of primary importance in many practical areas such as control, system identification, visual/audio tracking, experimental design, data mining, robust statistics and novelty/outlier detection. This paper presents a new method of kernel information matrix ellipsoid estimation (KIMEE) that finds an ellipsoid in a kernel defined feature space based on a centered information matrix. Although the method is very general and can be applied to many of the aforementioned problems, the main focus in this paper is the problem of novelty or outlier detection associated with fault detection. A simple iterative algorithm based on Titterington's minimum volume ellipsoid method is proposed for practical implementation. The KIMEE method demonstrates very good performance on a set of real-life and simulated datasets compared with support vector machine methods

A metaobject architecture for fault-tolerant distributed systems : the FRIENDS approach

The FRIENDS system developed at LAAS-CNRS is a metalevel architecture providing libraries of metaobjects for fault tolerance, secure communication, and group-based distributed applications. The use of metaobjects provides a nice separation of concerns between mechanisms and applications. Metaobjects can be used transparently by applications and can be composed according to the needs of a given application, a given architecture, and its underlying properties. In FRIENDS, metaobjects are used recursively to add new properties to applications. They are designed using an object oriented design method and implemented on top of basic system services. This paper describes the FRIENDS software-based architecture, the object-oriented development of metaobjects, the experiments that we have done, and summarizes the advantages and drawbacks of a metaobject approach for building fault-tolerant system