Fault diagnostic instrumentation design for environmental control and life support systems

As a development phase moves toward flight hardware, the system availability becomes an important design aspect which requires high reliability and maintainability. As part of continous development efforts, a program to evaluate, design, and demonstrate advanced instrumentation fault diagnostics was successfully completed. Fault tolerance designs for reliability and other instrumenation capabilities to increase maintainability were evaluated and studied

Continuous wavelet transform and neural network for condition monitoring of rotodynamic machinery

This paper describes a novel method of rotodynamic machine condition monitoring using a wavelet transform and a neural network. A continuous wavelet transform is applied to the signals collected from accelerometer. The transformed images are then extracted as unique characteristic features relating to the various types of machine conditions. In the experiment, four types of machine operating conditions have been investigated: a balanced shaft; an unbalanced shaft, a misaligned shaft and a defective bearing. The back propagation neural network (BPNN) is used as a tool to evaluate the performance of the proposed method. The experimental results result in a recognition rate of 90 percent

Major challenges in prognostics: study on benchmarking prognostic datasets

Even though prognostics has been defined to be one of the most difficult tasks in Condition Based Maintenance (CBM), many studies have reported promising results in recent years. The nature of the prognostics problem is different from diagnostics with its own challenges. There exist two major approaches to prognostics: data-driven and physics-based models. This paper aims to present the major challenges in both of these approaches by examining a number of published datasets for their suitability for analysis. Data-driven methods require sufficient samples that were run until failure whereas physics-based methods need physics of failure progression

UHF diagnostic monitoring techniques for power transformers

This paper initially gives an introduction to ultra-high frequency (UHF) partial discharge monitoring techniques and their application to gas insulated substations. Recent advances in the technique, covering its application to power transformers, are then discussed and illustrated by means of four site trials. Mounting and installation of the UHF sensors is described and measurements of electrical discharges inside transformers are presented in a range of formats, demonstrating the potential of the UHF method. A procedure for locating sources of electrical discharge is described and demonstrated by means of a practical example where a source of sparking on a tap changer lead was located to within 15 cm. Progress with the development of a prototype on-line monitoring and diagnostic system is reviewed and possible approaches to its utilization are discussed. New concepts for enhancing the capabilities of the UHF technique are presented, including the possibility of monitoring the internal mechanical integrity of plant. The research presented provides sufficient evidence to justify the installation of robust UHF sensors on transformer tanks to facilitate their monitoring if and when required during the service lifetime

A review of intelligent methods for condition monitoring and fault diagnosis of stator and rotor faults of induction machines

Nowadays, induction motor (IM) is extensively used in industry, including mechanical and electrical applications. However, three main types of IM faults have been discussed in the literature, bearing, stator, and rotor. Importantly, stator and rotor faults represent approximately 50%. Traditional condition monitoring (CM) and fault diagnosis (FD) methods require a high processing cost and much experience knowledge. To tackle this challenge, artificial intelligent (AI) based CM and FD techniques are extensively developed. However, there have been many review research papers for intelligent CM and FD machine learning methods of rolling elements bearings of IM in the literature. Whereas there is a lack in the literature, and there are not many review papers for both stator and rotor intelligent CM and FD. Thus, the proposed study's main contribution is in reviewing the CM and FD of IM, especially for the stator and the rotor, based on AI methods. The paper also provides discussions on the main challenges and possible future works