Methods for automatic condition monitoring

Abstract Reliable condition monitoring is crucial to successful maintenance operations in many industrial cases but adequate manpower and expertise to conduct proper monitoring is not always available. Suitable signal processing technologies can provide a considerable relief to this matter. Intelligent and adaptive systems can at their best detect the faults or even determine type and severity of the fault. Nowadays high-end technology for automatic monitoring is generally available at reasonable price, but many advanced methods are yet to be put into action. This paper discusses several methods which could relatively easily be built in to modern condition monitoring systems

Extracting periodically repeating shocks in a gearbox from simultaneously occurring random vibration

Abstract Periodically repeating shocks are a quite common indication of certain defect in machinery. Detecting these shocks in early stage, before the defect is severe enough to cause failure, can provide a huge advantage in maintenance planning. The earliest possible warning of a defect may be highly important, especially in targets where failure can lead to a vast loss of production or a safety risk. Shock-like vibrations are, however, usually rather faint when the defect is a minor one. This simply means that the shocks may often be too low in magnitude to be easily detected. In this paper, we use different techniques based on real order derivative to detect gear defects. Higher real order derivative, discrete Fourier transform, and Hilbert transform are discussed

Prediction of mechanical stress in roller leveler based on vibration measurements and steel strip properties

Abstract The continuous development of steel products generates new challenges for the maintenance of manufacturing machines in steel mills. Substantial mechanical stress is inflicted on the machines during the processing of modern high-strength steels. This increases the risks of damage and flaws in the processed material may appear if the capability of a machine is exceeded. Therefore, new approaches are needed to prevent the machine condition from deteriorating. This study introduces an approach to the prediction of mechanical stress inflicted on a roller leveler during the processing of cold steel strips. The relative stress level is indicated by features extracted from an acceleration signal. These features are based on the calculation of generalized norms. Steel strip properties are used as explanatory variables in regression models to predict values for the extracted vibration features. The models tested in this study include multiple linear regression, partial least squares regression and generalized regression neural network. The models were tested using an extensive data set from a roller leveler that is in continuous operation in a steel mill. The prediction accuracy of the best models identified indicates that the relative stress level inflicted by each steel strip could be predicted based on its properties

Automation of low-speed bearing fault diagnosis based on autocorrelation of time domain features

Abstract This study is focused on the application of automated techniques on low-speed bearing diagnostics. The diagnosis in low-speed conditions is hampered by the long periods between defect-related impulses and the high level of noise relative to the magnitude of the impulses. To detect a localised defect in such conditions, a new approach that uses vibration signals and information on the bearing defect frequencies is proposed. At first, the vibration signal is filtered in a specific frequency range to enable the detection of the impulses hidden in the signal. The filtered signal is then segmented into short time windows, the length of which are selected based on the bearing defect frequencies. Statistical time domain features are calculated from these windows to amplify and compress the impulses inflicted by the defect. Then, a criterion based on the autocorrelation values of specific time lags is calculated. An exhaustive search procedure is used to determine the frequency band for signal filtering and to select the statistical feature, which together maximises the proposed criterion. The highest value of the criterion is finally compared with the corresponding value from the baseline condition to detect the localised defect. The proposed technique is demonstrated on simulated signals, and validated based on the vibration signals from laboratory tests with undamaged, slightly damaged and severely damaged rolling elements in a rolling element bearing. Different conditions with shaft speeds from 20 to 80 rpm were studied in the laboratory tests. The proposed technique was compared with automated envelope spectrum diagnosis approaches based on the peak ratio and peak-to-median indicators and the fast kurtogram. The results reveal that the criterion based on autocorrelation gave defect indications associated with the correct type of defect in various circumstances while the tested envelope spectrum approaches were prone to induce an incorrect conclusion. Moreover, the results indicate that the approach could be used successfully on signals with a length that includes relatively few defect periods or impulses. The approach requires a high sampling rate relative to the defect frequencies, which may limit its suitability for the higher shaft speeds

Vibration‐based monitoring of gas‐stirring intensity in vacuum tank degassing

Abstract Liquid steel is typically stirred in a vacuum tank using argon gas injection to achieve a homogeneous composition and high‐purity steel. The aim of this work is to study the effect of vessel vibration on the operational state monitoring of the gas stirring in a vacuum tank degasser. Following an extensive analysis of vibration features, the root mean square (RMS) of vertical velocity is found to be the best feature for the measurement of the stirring intensity caused by the volumetric gas injection rate into the ladle. Smoothing is conducted using a centered median filter with a window length of 21 s. In this work, the operational state monitoring of gas stirring is described using a ladle responsiveness value (LRV). This describes the ability of a ladle to generate the maximum amount of vibration with the minimum amount of argon gas. The LRV summarized for each ladle reveals significant differences between them. Correspondingly, a rolling ladle responsiveness value (rLRV) is used for online monitoring of possible gas leakages. The rLRV can also be used for the online monitoring of the stirring efficiency and as its comparison with the overall efficiency of a specific ladle or all ladles