Sensing Linear Roller Guide Carriages for Condition Monitoring Applications

2021 ◽  
Author(s):  
Berend Denkena ◽  
Benjamin Bergmann ◽  
Per Schreiber ◽  
Alexander Schmidt ◽  
Markus Klaproth
Keyword(s):  
Condition Monitoring ◽  
Roller Guide ◽  
Monitoring Applications

On the use of jerk and snap in condition monitoring of machinery – review and case studies

2021 ◽  
Vol 63 (8) ◽  
pp. 457-464
Author(s):  
S Lahdelma
Keyword(s):  
Condition Monitoring ◽  
Large Scale ◽  
Early Stage ◽  
Time Derivative ◽  
Additional Insight ◽  
Rolling Bearings ◽  
Monitoring Applications ◽  
Derivatives Of ◽  
Insight Into ◽  
Peak Value

The time derivatives of acceleration offer a great advantage in detecting impact-causing faults at an early stage in condition monitoring applications. Defective rolling bearings and gears are common faults that cause impacts. This article is based on extensive real-world measurements, through which large-scale machines have been studied. Numerous laboratory experiments provide additional insight into the matter. A practical solution for detecting faults with as few features as possible is to measure the root mean square (RMS) velocity according to the standards in the frequency range from 10 Hz to 1000 Hz and the peak value of the second time derivative of acceleration, ie snap. Measuring snap produces good results even when the upper cut-off frequency is as low as 2 kHz or slightly higher. This is valuable information when planning the mounting of accelerometers.

Case Studies of Proactive Condition Monitoring Applications

2014 ◽  
pp. 137-183
Author(s):  
Zhaklina Stamboliska ◽  
Eugeniusz Rusiński ◽  
Przemyslaw Moczko
Keyword(s):  
Case Studies ◽  
Condition Monitoring ◽  
Monitoring Applications

Open Platform for the Integration of Monitoring Applications

2002 ◽  
Author(s):  
Andreas Kahmen ◽  
Manfred Weck
Keyword(s):  
Control System ◽  
Machine Tool ◽  
Real Time ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Integration Platform ◽  
Open Platform ◽  
Tool Condition ◽  
Monitoring Applications ◽  
Monitoring Software

Process and machine tool condition monitoring are the keys to an increasing degree of automation and consequently to an increasing productivity in manufacturing. The realisation of monitoring functionality demands an extension of the control system. The prerequisite for these extensions are open interfaces in the NC-kernel. Nowadays controls with open NC-kernel interfaces are available on the market. However these interfaces are vendor specific solutions that do not allow the reuse of monitoring software in different controls. To overcome these limitations a platform with vendor neutral open real-time interface for the integration of monitoring functionality into the NC-kernel is presented in this paper. Additionally two realisations of the integration platform for different target systems are described.

scholarly journals A Data Filter for Identifying Steady-State Operating Points in Engine Flight Data for Condition Monitoring Applications

2010 ◽  
Author(s):  
Donald L. Simon ◽  
Jonathan S. Litt
Keyword(s):  
Steady State ◽  
Standard Deviation ◽  
Condition Monitoring ◽  
Measurement Data ◽  
Streaming Data ◽  
State Data ◽  
Flight Data ◽  
Monitoring Applications ◽  
The Mean ◽  
Operating Points

This paper presents an algorithm that automatically identifies and extracts steady-state engine operating points from engine flight data. It calculates the mean and standard deviation of select parameters contained in the incoming flight data stream. If the standard deviation of the data falls below defined constraints, the engine is assumed to be at a steady-state operating point, and the mean measurement data at that point are archived for subsequent condition monitoring purposes. The fundamental design of the steady-state data filter is completely generic and applicable for any dynamic system. Additional domain-specific logic constraints are applied to reduce data outliers and variance within the collected steady-state data. The filter is designed for on-line real-time processing of streaming data as opposed to post-processing of the data in batch mode. Results of applying the steady-state data filter to recorded helicopter engine flight data are shown, demonstrating its utility for engine condition monitoring applications.

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