Vibration Analysis Based on HJ-Biplots

Abstract

Vibration Analysis (VA) is now routinely used for condition monitoring and failure diagnosis in Condition Based Maintenance (CBM). In the context of VA, a methodology is proposed, based on biplots, to simultaneously display both vibration frequencies and their measurement points, in support of monitoring and diagnostics tasks.In this research, real observational data obtained measuring mechanical vibrations on four generators aboard a Portuguese Navy Ship in real operating conditions is used. A portable vibration collector was employed, and the measurements were taken at 13 measurement points in each one of four generators, using the same collector settings. Spectrograms resulting from vibration measurements were transformed into biplots and used for decision support according to the proposed methodology. Data analysis showed a robust stability in the macrostructure of biplots when observations resulting from different generators of the same model and at the same assumed conditions was analyzed. This invariance allows the specification of reference conditions, rules to detect changes of operating conditions and the emergence of failures. The proposed methodology, once embedded in dedicated software, will reduce the interpretation error in diagnosis and prognosis associated to variability in personnel training and experience. Consequently, it will increase the safe use of VA in an increasing number of situations.

Diesel Engine Fault Prediction Using Artificial Intelligence Regression Methods

“…Since hypothesis h can be seen as a certain regressor configuration, motivated by training with a specific portion of the dataset, where h[x(n)] is the adjustment of the regression variable, for each of the K successive K-fold implementations of the crossvalidation step, the average value of RMSE (µ RMSE ) after K implementations is described by Equation ( 17), and the associated standard deviation of the RMSE (σ RMSE ) is detailed in Equation (18).…”

“…Recently, many methods have been proposed to detect diesel engine faults, which include oil analysis [15], thermodynamic parameters [16], and vibration analysis [17,18], among others. Furthermore, advances in the development of automation and instrumentation technology have led to the development of efficient monitoring devices [19], contributing to predictive maintenance techniques.…”