Data Mining - Concepts and Techniques.

A review on machinery diagnostics and prognostics implementing condition-based maintenance

A review on empirical mode decomposition in fault diagnosis of rotating machinery

Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications

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Introduction To Robust Estimation And Hypothesis Testing

Condition-Based maintenance (CBM) becomes more and more popular in industry. The idea is simple: measure raw data (vibrations, temperatures, etc.), extract features and make a right decisions regarding replacement of the whole machine or its component at appropriate time. Right decision might mean simple if-then-else rule or complex decision making scheme using multidimensional data. In any case mentioned rules require definition of appropriate thresholds for diagnostic parameters (i.e. decision boundaries). This is a key problem in CBM. The article presents the procedure for determining decision thresholds based on statistical modeling of diagnostic data. In the presented procedure first we fit the suitable distribution (Weibull) to data set for each gearbox. Next we calculate the fitting quality measure and select the distribution parameters for well fitted data. Finally, on the basis of the multidimensional analysis of those parameters we determine threshold values for the effective identification of the machines’ condition and their components. It might be interpreted as training process of diagnostic system. From this phase of the procedure we can obtain thresholds for warning and alarm statuses and they can be used for classification of rest of the data (that did not pass modeling phase). Proposed procedure has been applied to relatively large diagnostic data set that covers nearly 150 measurements acquired during several years in underground mine. The data describes gearboxes in different conditions—from nearly new or after repair to seriously damaged/worn just before failure.

Diagnostic Features Modeling for Decision Boundaries Calculation for Maintenance of Gearboxes Used in Belt Conveyor System

In the paper is presented the object and operation factors oriented diagnostic. This idea is developed by the author in resent times. In the idea detailed machine/object design properties are taken into consideration. Four groups of factors are taken into consideration namely: design, production technology, operation, and change of condition. The system is treated as a unit or divided into subunits and degradation process is included into consideration. The further interpretations of the concept of load susceptibility of machine systems is explored. The paper is a kind of shortened summary on the use of the concept of load susceptibility for machinery condition monitoring.

Object and Operation Factor Oriented Diagnostics

The paper deals with mathematical modelling and computer simulation of a gearbox driving system with a double stage gearbox. Mathematical modelling and computer simulations are used for supporting diagnostic inference. Vibration is thought of as a signal of gear condition. It is stressed that vibration generated by gears is influenced by many factors. These factors are divided into four groups: design, production technology, operational, condition change. The condition change of a gearbox is given by gear faults that are divided into single faults such as a tooth crack or breakage or distributed faults as pitting, scuffing, and erosion. The faults are modelled in the case of a crack as a change of tooth stiffness in the case of distributed faults they are given multi-parameter functions. Simulated signals undergo signal analysis by spectrum, cepstrum, time-frequency spectrogram. It has been shown by computer simulation that single and distributed faults are identified by cepstrum. For explicit fault identification time-frequency spectrogram has to be additionally used. The computer simulation results are confirmed by analysis of measured vibration signals received from a gearbox wall/housing. The aim of mathematical modelling and computer simulation, besides finding the relationship between gear condition and vibration signal is in the future to give vibration signals for neural network training.Copyright © 2003 by ASME

Gearbox Systems Dynamic Modelling for Diagnostic Fault Detection

In the paper a new diagnostic approach for gearbox used in belt conveyors will be discussed. The purpose of the work is to provide novel view on diagnostic data processing in the context of detection of changes in condition for population of gearboxes used in belt conveyor network. The idea will be presented by examples: a data base of diagnostic features collected during last 3 years (real data from conveyors operating in mining company) will be used for illustration.

Novel Techniques of Diagnostic Data Processing for Belt Conveyor Maintenance

Condition monitoring of machines working under non-stationary operations is a one of the most challenging problems in maintenance. Wind turbine is an example of such class of machines. As it was suggested by Bartelmus and Zimroz, one of effective approach may be to identify operating conditions and investigate their influence on used diagnostic features. Commonly used methods based on measurement electric current, rotational speed, power and other process variables requires additional equipment (sensors, acquisition cards) and software. It is proposed to use advanced signal processing techniques for instantaneous shaft speed recovery from vibration signal. It may be used instead of extra channels or in parallel as signal verification.

Walter Bartelmus

Papers

Diagnostic Features Modeling for Decision Boundaries Calculation for Maintenance of Gearboxes Used in Belt Conveyor System

Object and Operation Factor Oriented Diagnostics

Gearbox Systems Dynamic Modelling for Diagnostic Fault Detection

Novel Techniques of Diagnostic Data Processing for Belt Conveyor Maintenance

Measurement of instantaneous shaft speed by advanced vibration signal processing - application to wind turbine gearbox