The Detection of Shaft Misalignments using Motor Current Signals from a Sensorless Variable Speed Drive

Shaft misalignments are common problems in rotating machines which cause additional dynamic and static loads, and vibrations in the system, leading to early damages and energy loss. It has been shown previously that it is possible to use motor current signature analysis to detect and diagnose this fault in motor drives. However, with a variable speed drive (VSD) system, it becomes dif-ficult to detect faults as the drive compensates for the small changes from fault ef-fects and increased noise in the measured data. In this paper, motor current signa-tures including dynamic and static data have been investigated for misalignment diagnosis in a VSD system. The study has made a systemic comparison of differ-ent control parameters between two common operation modes: open loop and sen-sorless control. Results show that fault detection features on the motor current from the sensorless mode can be the same as those of the open loop mode, however, the detection and diagnosis is significantly more difficult. In contrast, because of the additional frictional load, features from static data show results of early detection and diagnosis of different degrees of misalignment is as good as that from conventional vibration methods

Research on Fault Diagnosis Based on Dynamic causality diagram and Fuzzy Reasoning Fusion Method

With the progress of urbanization, the demand for elevators has upgraded from safe operation to comfortable, efficient, and all-round demand. The abnormal operation of the elevator is difficult to diagnose due to the complexity of the fault. This paper proposes a fault diagnosis method based on dynamic causality diagram and fuzzy reasoning. The dynamic causality diagram is extended, the intermediate module nodes are added, the description of the intermediate process of the elevator control system is solved, and the complete expression of knowledge is realized. The control timing of the elevator operation is introduced into the network structure of the dynamic causality diagram, which enhances the dynamic characteristics of the network. The causal cycle logic of the dynamic causality diagram is used to represent input and output signals and faults in elevator control systems. In the update of fuzzy rules, the real-time of fuzzy reasoning is enhanced, the search space of fuzzy rule matching is reduced, and the efficiency is improved. This paper combines actual field measurements and experimental data for fault diagnosis. Finally, the simulation, diagnosis and maintenance decision of the fault are realized, and an intelligent solution for elevator fault diagnosis is further proposed

Application of simulation to mechanical fault diagnosis by pattern matching with parity equations

[Abstract] The operation of technical processes requires increasingly advanced supervision and fault diagnosis procedures to improve reliability, safety and global efficiency. Simulation associated to signal analysis techniques offers an alternative solution to the field of detection and diagnosis. This paper deals with a basic method in supervision and fault diagnosis of mechanical equipment using simulation procedures to determine both, the ideal dynamic pattern and the faulty dynamic pattern. Then comparison of actual vibration spectrum with both patterns for extract a valid conclusion is the aim of thi

A diagnosis system using object-oriented fault tree models

Spaceborne computing systems must provide reliable, continuous operation for extended periods. Due to weight, power, and volume constraints, these systems must manage resources very effectively. A fault diagnosis algorithm is described which enables fast and flexible diagnoses in the dynamic distributed computing environments planned for future space missions. The algorithm uses a knowledge base that is easily changed and updated to reflect current system status. Augmented fault trees represented in an object-oriented form provide deep system knowledge that is easy to access and revise as a system changes. Given such a fault tree, a set of failure events that have occurred, and a set of failure events that have not occurred, this diagnosis system uses forward and backward chaining to propagate causal and temporal information about other failure events in the system being diagnosed. Once the system has established temporal and causal constraints, it reasons backward from heuristically selected failure events to find a set of basic failure events which are a likely cause of the occurrence of the top failure event in the fault tree. The diagnosis system has been implemented in common LISP using Flavors

Hardware-In-The-Loop Assessment of Fuzzy and Neural Network Fault Diagnosis Schemes for a Wind Turbine Model

The fault diagnosis of wind turbines includes extremely challenging aspects that motivate the research issues considered in this paper. In particular, this work studies fault diagnosis solutions that are considered in a viable way and used as advanced techniques for condition monitoring of dynamic processes. To this end, the work proposes the design of fault diagnosis techniques that exploits the estimation of the fault by means of data-driven approaches. These fuzzy and neural network structures are integrated with auto-regressive with exogenous input regressors, thus making them able to approximate unknown nonlinear dynamic functions with arbitrary degree of accuracy. The capabilities of fault diagnosis schemes are validated by using a real-time simulator of a wind turbine system. Moreover, at this stage the benchmark is also useful to analyse the robustness and the reliability characteristics of the developed tools in the presence of model-reality mismatch and modelling error effects featured by the wind turbine simulator. This realistic simulator relies on a hardware-in-the-loop tool that is finally implemented for verifying and validating the performance of the developed fault diagnosis strategies in an actual environment

Online Condition Monitoring of Electric Powertrains using Machine Learning and Data Fusion

Safe and reliable operations of industrial machines are highly prioritized in industry. Typical industrial machines are complex systems, including electric motors, gearboxes and loads. A fault in critical industrial machines may lead to catastrophic failures, service interruptions and productivity losses, thus condition monitoring systems are necessary in such machines. The conventional condition monitoring or fault diagnosis systems using signal processing, time and frequency domain analysis of vibration or current signals are widely used in industry, requiring expensive and professional fault analysis team. Further, the traditional diagnosis methods mainly focus on single components in steady-state operations. Under dynamic operating conditions, the measured quantities are non-stationary, thus those methods cannot provide reliable diagnosis results for complex gearbox based powertrains, especially in multiple fault contexts. In this dissertation, four main research topics or problems in condition monitoring of gearboxes and powertrains have been identified, and novel solutions are provided based on data-driven approach. The first research problem focuses on bearing fault diagnosis at early stages and dynamic working conditions. The second problem is to increase the robustness of gearbox mixed fault diagnosis under noise conditions. Mixed fault diagnosis in variable speeds and loads has been considered as third problem. Finally, the limitation of labelled training or historical failure data in industry is identified as the main challenge for implementing data-driven algorithms. To address mentioned problems, this study aims to propose data-driven fault diagnosis schemes based on order tracking, unsupervised and supervised machine learning, and data fusion. All the proposed fault diagnosis schemes are tested with experimental data, and key features of the proposed solutions are highlighted with comparative studies.publishedVersio

An innovations approach to fault diagnosis in linear time-varying descriptor systems

International audienceIn this paper fault diagnosis is studied for linear time varying descriptor systems, the discrete time counterpart of dynamic systems described by differential-algebraic equations. The Kalman filter for descriptor systems is first revisited by completing existing results about its properties that are essential for the purpose of fault diagnosis. Based on the analysis of the effects of the considered actuator and sensor faults on the innovation of the Kalman filter, it is shown that the considered fault diagnosis problem in linear time varying descriptor systems is equivalent to a classical linear regression problem formulated by appropriately filtering the input-output data. Following this result, algorithms for fault diagnosis through maximum likelihood estimation are then proposed

Case-based reasoning combined with statistics for diagnostics and prognosis

Many approaches used for diagnostics today are based on a precise model. This excludes diagnostics of many complex types of machinery that cannot be modelled and simulated easily or without great effort. Our aim is to show that by including human experience it is possible to diagnose complex machinery when there is no or limited models or simulations available. This also enables diagnostics in a dynamic application where conditions change and new cases are often added. In fact every new solved case increases the diagnostic power of the system. We present a number of successful projects where we have used feature extraction together with case-based reasoning to diagnose faults in industrial robots, welding, cutting machinery and we also present our latest project for diagnosing transmissions by combining Case-Based Reasoning (CBR) with statistics. We view the fault diagnosis process as three consecutive steps. In the first step, sensor fault signals from machines and/or input from human operators are collected. Then, the second step consists of extracting relevant fault features. In the final diagnosis/prognosis step, status and faults are identified and classified. We view prognosis as a special case of diagnosis where the prognosis module predicts a stream of future features

A Versatile Simulation Environment of FTC Architectures for Large Transport Aircraft

We present a simulation environment with 3-D stereo visualization facilities destined for an easy setup and versatile assessment of fault detection and diagnosis based fault tolerant control systems. This environment has been primarily developed as a technology demonstrator of advanced reconfigurable flight control systems and is based on a realistic six degree of freedom flexible aircraft model. The aircraft control system architecture includes a flexible fault detection and diagnosis system and a reconfigurable nonlinear dynamic inversion based controller, able to handle different fault situations