Diagnosis of Combination Faults in a Planetary Gearbox using a Modulation Signal Bispectrum based Sideband Estimator

This paper presents a novel method for diagnosing combination faults in planetary gearboxes. Vibration signals measured on the gearbox housing exhibit complicated characteristics because of multiple modulations of concurrent excitation sources, signal paths and noise. To separate these modulations accurately, a modulation signal bispectrum based sideband estimator (MSB-SE) developed recently is used to achieve a sparse representation for the complicated signal contents, which allows effective enhancement of various sidebands for accurate diagnostic information. Applying the proposed method to diagnose an industrial planetary gearbox which coexists both bearing faults and gear faults shows that the different severities of the faults can be separated reliably under different load conditions, confirming the superior performance of this MSB-SE based diagnosis scheme

Argumentation-based fault diagnosis for home networks

Home networks are a fast growing market but managing them is a difficult task, and diagnosing faults is even more challenging. Current fault management tools provide comprehensive information about the network and the devices but it is left to the user to interpret and reason about the data and experiment in order to find the cause of a problem. Home users may not have motivation or time to learn the required skills. Furthermore current tools adopt a closed approach which hardcodes a knowledge base, making them hard to update and extend. This paper proposes an open fault management framework for home networks, whose goal is to simplify network troubleshooting for non-expert users. The framework is based on assumption-based argumentation that is an AI technique for knowledge representation and reasoning. With the underlying argumentation theory, we can easily capture and model the diagnosis procedures of network administrators. The framework is rule-based and extensible, allowing new rules to be added into the knowledge base and diagnostic strategies to be updated on the fly.The framework can also utilise external knowledge and make distributed diagnosi

Detection and Diagnosis of Motor Stator Faults using Electric Signals from Variable Speed Drives

Motor current signature analysis has been investigated widely for diagnosing faults of induction motors. However, most of these studies are based on open loop drives. This paper examines the performance of diagnosing motor stator faults under both open and closed loop operation modes. It examines the effectiveness of conventional diagnosis features in both motor current and voltage signals using spectrum analysis. Evaluation results show that the stator fault causes an increase in the sideband amplitude of motor current signature only when the motor is under the open loop control. However, the increase in sidebands can be observed in both the current and voltage signals under the sensorless control mode, showing that it is more promising in diagnosing the stator faults under the sensorless control operation

Flight deck engine advisor

The focus of this project is on alerting pilots to impending events in such a way as to provide the additional time required for the crew to make critical decisions concerning non-normal operations. The project addresses pilots' need for support in diagnosis and trend monitoring of faults as they affect decisions that must be made within the context of the current flight. Monitoring and diagnostic modules developed under the NASA Faultfinder program were restructured and enhanced using input data from an engine model and real engine fault data. Fault scenarios were prepared to support knowledge base development activities on the MONITAUR and DRAPhyS modules of Faultfinder. An analysis of the information requirements for fault management was included in each scenario. A conceptual framework was developed for systematic evaluation of the impact of context variables on pilot action alternatives as a function of event/fault combinations

Fault Detection and Diagnosis in Air Conditioners and Refrigerators

A fault detection and diagnosis (FDD) method was used to detect and diagnose faults on both a refrigerator and an air conditioner during normal cycling operation. The objective of the method is to identify a set of sensors that can detect faults reliably before they severely hinder system performance. Unlike other methods, this one depends on the accuracy of a number of small, on-line linear models, each of which is valid over a limited range of operating conditions. To detect N faults, N sensors are needed. Using M>N sensors can further reduce the risk of false positives. For both the refrigerator and air conditioner systems, about 1000 combinations of candidate sensor locations were examined. Through inspection of matrix condition numbers and each sensor's contribution to fault detection calculation, the highest quality sets of sensors were identified. The issue of detecting simultaneous multiple faults was also addressed, with varying success. Fault detection was verified using both model simulations and experimental data. The results were similar, although in practice only one of the two would probably be used. Both load-type faults (such as door gasket leaks) and system faults were simulated on the refrigerator. It was found that system faults were generally more easily detectable than load faults. Refrigerator experiments were performed on a typical household refrigerator because it was readily available in a laboratory, but the results of this project may be more immediately useful on larger commercial, industrial or transport refrigeration systems. Air conditioner experiments were performed on a 3-ton split system. Again, the economic benefits of this type of fault detection scheme may also be more feasible for larger field-assembled systems.Air Conditioning and Refrigeration Project 8