Condition Monitoring Indicators Research Articles

Generalized Transmissibility Damage Indicator With Application to Wind Turbine Component Condition Monitoring

Frequency methods such as frequency spectrum analysis, frequency spike detection, demodulation, envelope spectrum method have been widely used for condition monitoring of engineering structural systems. Different from the conventional frequency methods, the transmissibility function (TF) represents the relationship between different system output responses such as, e.g., vibration and acoustic emission sensor measurements. This paper introduces a simple and effective generalized transmissibility damage indicator (GTDI) for TF-based condition monitoring. Unlike the conventional transmissibility damage indicator, the new GTDI can improve the detection sensitivity, reduces noise effects and avoid dynamic loadings effects. This is achieved by combining multiple groups of data to obtain more accurate transmissibility analysis, exploiting all the available TFs, and using multiple references. This has two advantages. First, it does not require any other priori knowledge about the system responses. Therefore, the method can be used for the condition monitoring of a wide range of components or systems. Furthermore, the method can be easily implemented using fast Fourier transform or power spectra density methods and therefore is computationally efficient. These make the method very suitable for implementing online real-time condition monitoring. The method is investigated by simulation studies and then applied to analyze the vibration data of the main bearing of operating wind turbines, producing very promising results.

Acoustic Emission Signal Analysis for Tool Condition Monitoring in Microendmilling of Aluminium Alloy

In this study, the effect of tool wear is correlated with acoustic emission (AE) signal during microendmilling of aluminium alloy (AA 1100). The AE signals were acquired using Kistler make AE sensor and the signal features are analyzed in time domain (root mean square (RMS)) and frequency domain (dominant frequency and amplitude). The dominant frequency of the AE signal shows increasing trend with increase in the tool wear, where as AERMS show uneven trend. The discrete wavelet transformation technique (DWT) has also been carried out by decomposing the required AE signal in different frequency bands. The AERMS and specific AE energy were computed for the decomposed AE signals. From the specific AE energy, it is observed that shearing occurs during microendmilling and also found to be similar that of macro-regieme endmilling. The result demonstrated that the AE signals are potential indicator for tool condition monitoring in microendmilling.

Estimating Mean Residual Life for a Case Study of Rail Wagon Bearings

This paper develops a prognostics model to estimate the Mean Residual Life of Rail Wagon Bearings within certain confidence intervals. The prognostics model is constructed using a Proportional Hazards approach, informed by imperfect data from a bearing acoustic monitoring system, and a failure database. The model supports prediction within a defined maintenance planning window from the time of receipt of the latest acoustic condition monitoring information. We use the model to decide whether to replace a bearing, or leave it until collection of the next condition monitoring indicators. The model is tested on a limited number of cases, and demonstrates good predictive capability. Opportunities to improve the performance of the model are identified, and the processes necessary and time required to build the model are described. Lessons learned from dealing with real field data will assist those interested in using prognostics to support maintenance planning activities.

Mechanical fault detection using fuzzy index fusion

This paper reports a simple, effective and robust fusion approach based on fuzzy logic and Sugeno-style inference engine. Using this method, four condition-monitoring indicators, developed for detection of transient and gradual abnormalities, are fused into one single comprehensive fuzzy fused index (FFI) for reliable machinery health assessment. This approach has been successfully tested and validated in two different applications: tool condition monitoring in milling operations and bearing condition assessment. The FFI differentiates clearly between the normal and abnormal conditions using the same fuzzy rule base. This certainly shows the versatility and robustness of the FFI. As the FFI value always falls between zero and one, it facilitates threshold setting in monitoring conditions of different tools or machinery components. Our experimental study also indicates that the FFI is sensitive to fault severity, capable of differentiating damages caused by an identical fault at different bearing components, but not susceptible to load changes.