Abstract: A monitoring device for monitoring a bearing in an electromagnetic field environment includes a vibration sensor for delivering at least one measurement that includes at least one vibration frequency generated by a bearing, and a processing module that includes a first processing module configured to determine at least one normalized measured value from the at least one vibration frequency, a filtering module configured to compare the normalized measured value to at least one predetermined normalized reference value indicative of electromagnetic phenomena acting on the bearing, and a second processing module configured to process the at least one vibration frequency to determine a bearing defect if the normalized measured value is different from the at least one predetermined normalized reference value.

Abstract: A monitoring device for monitoring a bearing in an electromagnetic field environment includes a vibration sensor for delivering at least one measurement that includes at least one vibration frequency generated by a bearing, and a processing module that includes a first processing module configured to determine at least one normalized measured value from the at least one vibration frequency, a filtering module configured to compare the normalized measured value to at least one predetermined normalized reference value indicative of electromagnetic phenomena acting on the bearing, and a second processing module configured to process the at least one vibration frequency to determine a bearing defect if the normalized measured value is different from the at least one predetermined normalized reference value.

Abstract: A method of identifying at least one defect of a rotating component, where the defect is selected from a group of predefined defects, includes extracting frequency data related to each of the predefined defects in order to form a first spectrum signature for each of the predefined defects. Also measuring at least one vibration signal produced by the rotating component to obtain at least two second spectrums, filtering each second spectrum based on an exponential smoothing algorithm, selecting peaks in each second spectrum according to a prominence of each of the peaks, setting selected peaks to zero if the selected peaks are not present in a predefined number of consecutive second spectrums, and calculating a probability that each first spectrum signature corresponds to at least one of the second spectrums.

Abstract: A method for detecting a characteristic of a machine or component according to a time and characteristic value record includes the steps of converting a time and characteristic value record of data samples of the machine or component in a selected time window to a two-dimensional pixel bitmap in a specified pixel window, horizontal coordinates of pixel points characterizing a time sequence of data samples and vertical coordinates of pixel points characterizing quantified characteristic values of the samples, the quantified characteristic values being pixel values converted from characteristic values of individual samples using a maximum value in the window as a standard; marking the pixel bitmap according to an existing conclusion to form a sample set for training a machine learning intelligent algorithm model; and using a pixel bitmap sample in the sample set to train the algorithm model; and using the model to detect from newly collected data samples.

Abstract: A method for predicting a remaining useful life of a machine on the basis of a data record, according to the following steps. Step 1) using regression analysis to fit a mathematical model of a machine life curve reflecting a variable relationship between time and a characteristic value, and calculating the time needed for the life curve to reach a preset failure threshold, and step 2) repeating step 1 above with a portion of data randomly omitted, and obtaining statistically a probability distribution of the expected RUL according to a repetition result. The RUL corresponding to the maximum probability distribution is determined to be the most likely expected RUL of the machine. The above method avoids bias in the prediction of machine RUL using a single life curve model, and can significantly improve the reliability and accuracy of machine prediction.

Abstract: A method for detecting a characteristic of a machine or component according to a time and characteristic value record includes the steps of converting a time and characteristic value record of data samples of the machine or component in a selected time window to a two-dimensional pixel bitmap in a specified pixel window, horizontal coordinates of pixel points characterizing a time sequence of data samples and vertical coordinates of pixel points characterizing quantified characteristic values of the samples, the quantified characteristic values being pixel values converted from characteristic values of individual samples using a maximum value in the window as a standard; marking the pixel bitmap according to an existing conclusion to form a sample set for training a machine learning intelligent algorithm model; and using a pixel bitmap sample in the sample set to train the algorithm model; and using the model to detect from newly collected data samples.