Patent ID: 12188812

The figures are only presented for indicative purposes and in no way limit the invention.

For greater clarity, identical or similar elements are marked by identical reference signs in all of the figures.

DETAILED DESCRIPTION OF AT LEAST ONE EMBODIMENT

FIG.1represents an embodiment of a monitoring device100according to the invention. The monitoring device100is intended to be installed on a machine that generates mechanical vibrations while it is in operation. The machine may for example be a motor, a pump, a compressor, a fan or a turbine. The monitoring device100comprises a vibration sensor110, a control module120, a memory130, a data transmission module140, an electrical supply150and an activation member160.

The vibration sensor110is adapted and configured to acquire a vibratory signal generated by the machine. The vibration sensor110is for example a 3-axis accelerometer. Such an accelerometer can acquire a vibratory signal comprising three components, each corresponding to one axis of the accelerometer. In other words, the vibratory signal may be represented in a three dimensional orthonormal reference frame formed by the three axes of the accelerometer. Advantageously, the three components of the vibratory signal are added together then normalised in order to facilitate the use of the monitoring device100. Indeed, by proceeding in this manner, the monitoring device100may be installed on the machine in any direction.

The vibratory signal obtained by the vibration sensor110is transmitted to the control module120, the role of which is to analyse the vibratory signal received. The control module120is notably configured to determine a vibratory signature from the vibratory signal acquired by the vibration sensor110, as described in greater detail hereafter. The control module120comprises for example a microprocessor.

The memory130is adapted and configured to save vibratory signatures characteristic of normal operation of the machine. The set of vibratory signatures stored in the memory forms a knowledge.

The data transmission module140is adapted and configured to transmit analysis reports in a periodic manner, as well as warnings when anomalies are detected. These data are for example transmitted to a monitoring console which makes it possible to view the analysis reports and to generate warnings. Preferably, the transmission module uses a wireless communication network of IOT (Internet Of Things) type. The IOT network may for example use LoRa technology or Sigfox technology.

The electrical supply150supplies the electrical energy necessary for the operation of the monitoring device100. The electrical supply150is advantageously dimensioned so that the monitoring device operates without interruption for an extended period of time, of the order of several years. In order to reduce electrical energy consumption, the monitoring device100is advantageously configured to go into sleep mode between each acquisition. The electrical supply150comprises for example a battery or a fuel cell.

The activation member160may be used to start or stop the monitoring device100. Advantageously, the monitoring device100comprises a single activation member160, which facilitates the use of the monitoring device100. Once the activation member160is triggered, the monitoring device100operates in a completely autonomous manner. The activation member160is for example a push button or a switch.

The monitoring device100also comprises fastening means (not represented) configured to maintain the monitoring device100on the machine. The fastening means may be magnetic elements or adhesive elements.

FIG.2shows a preferential embodiment of a monitoring method200according to the invention. The monitoring method200may be implemented by the monitoring device100ofFIG.1. The monitoring method200comprises a learning phase F1having the aim of constituting the knowledge and a monitoring phase F2during which the vibrations of the machine are compared with the knowledge in order to detect differences being able to be indicative of an anomaly in the machine. The anomaly may for example correspond to unbalanced, misaligned, poorly adjusted or worn parts of the machine. The monitoring method200may thus be used to carry out predictive maintenance.

The learning phase F1comprises a first step E1of acquisition in the temporal domain of a first vibratory signal generated by the machine. The vibratory signal is a sample of the vibrations of the machine corresponding to a first acquisition duration preferably comprised between 1 second and 5 seconds. The first acquisition duration is for example equal to 2 seconds. The first acquisition step E1is carried out at a sampling frequency that is advantageously parameterised so as to be greater than double the maximum working frequency of the vibration sensor. As a non-limiting example, the accelerometer used in the monitoring device100ofFIG.1is able to measure vibrations at frequencies comprised between 0 Hz and 200 Hz.

The first acquisition step E1is followed by a first step E2of determination of a vibratory signature corresponding to the vibratory signal acquired. The vibratory signature is for example determined in the following manner. The vibratory signal in the temporal domain may be transformed into a vibratory signal in the frequency domain, by performing for example a Fourier transform. The vibratory signal in the frequency domain extends over a frequency range which depends on the sampling frequency. This frequency range is divided into a predetermined number of intervals, for example equal to 128 or to 256. A coefficient is assigned to each frequency interval, each coefficient representing the intensity of the vibratory signal in the corresponding frequency interval. The vibratory signature thus comprises a series of values that characterise the vibratory signal taken on the machine.

A first step E3is next carried out of determination of a first level of similarity SR1between the vibratory signature that has just been determined and the vibratory signatures present in the knowledge. Methods for calculating the level of similarity are known to those skilled in the art and thus will not be described in greater detail. Such methods are notably used in the fingerprint recognition field. As a non-limiting example, the level of similarity may be determined by a distance calculation in an N-dimension space where N is here equal to the number of coefficients that the vibratory signature comprises. The level of similarity may be expressed in percentage.

The first level of similarity SR1is next compared, in the course of a first comparison step E4, with a first predetermined threshold TH1. If the first level of similarity SR1is less than the first threshold TH1, the first vibratory signature is considered as not being known. In this case, the first vibratory signature is saved in the knowledge in the course of a first saving step E5. Conversely, if the first level of similarity SR1is greater than the first threshold TH1, the first vibratory signature is considered as being known. In this case, the first vibratory signature is not saved in the knowledge. The first threshold TH1is for example set at 90%.

The steps of the learning phase F1are periodically repeated according to a first repetition period. The knowledge, which is initially empty, is thus constituted progressively. The first repetition period is for example equal to one minute. In other words, a new vibratory signal is sampled every minute. Preferably, the first repetition period is regular.

Advantageously, the learning phase F1also comprises a specific step of comparison E6of the first level of similarity of a predetermined number M of vibratory signatures determined consecutively with a specific predetermined threshold TH0greater than the first threshold TH1. The specific comparison step E6is for example carried out before the first step E4of comparison of the level of similarity with the first threshold TH1. If the first level of similarity SR1of the last M vibratory signatures is greater than the second threshold THO, the knowledge is considered as being constituted. In this case, the learning phase F1ends and the monitoring method200passes to the monitoring phase F2. If this is not the case, that is to say if the level of similarity of at least one of the last M vibratory signatures is less than the second threshold THO, the learning phase F1continues. As a non-limiting example, the learning phase F1may end when the level of similarity of the last ten vibratory signatures, which thus correspond to the last ten vibratory signals acquired, is above 98%. The duration of the learning phase F1depends on the machine on which the monitoring device100is installed but, in all cases, the learning phase F1is completed in an autonomous manner.

According to another embodiment, the knowledge may be considered as being constituted at the end of a predetermined learning duration. The learning duration may in this case be parameterised as a function of the type of machine on which the monitoring device100is intended to be installed.

Advantageously, the learning phase F1is preceded by an initial step E0of calibration of the vibration sensor. Indeed, the vibration sensor may comprise several nominal operating ranges. As a non-limiting example, the accelerometer used in the monitoring device100ofFIG.1comprises four nominal operating ranges which extend from 0 G to respectively 2, 4, 8 or 16 G. The aim of the calibration step E0is to determine which nominal range to use so as to obtain the most relevant data.

FIG.3shows a flowchart of the calibration step E0of the monitoring method200ofFIG.2. The calibration step E0begins with a first operation OP1of selection of the smallest nominal range of the vibration sensor. A vibratory signal is next taken on the machine in the course of a second operation OP2. In the course of a third operation OP3, it is verified if the vibratory signal is saturated. If such is the case, the nominal range of the vibration sensor is increased in the course of a fourth operation OP4. If not, the selected nominal range is appropriate and the calibration step E0ends. Preferably, the second operation OP2of acquisition of the vibratory signal is carried out over a relatively long duration, for example over a duration of one minute, so as to obtain a sufficiently representative sample of vibrations.

The learning phase F1is followed by the monitoring phase F2which comprises a second step E1′ of acquisition of a second vibratory signal, a second step E2′ of determination of a second vibratory signature corresponding to the second vibratory signal and a second step E3′ of determination of a second level of similarity of the second vibratory signature. These second steps E1′, E2′, E3′ carried out during the monitoring phase F2are similar to the first steps E1, E2, E3of same name carried out during the learning phase F1. The duration of the second acquisition step E1′ is preferably identical to the duration of the first acquisition step E1. Alternatively, the duration of the acquisition steps E1, E1′ may be different.

During the monitoring phase F2, the second level of similarity SR2is compared, in the course of a second comparison step E4′, with a second predetermined threshold TH2less than the first threshold TH1. If the second level of similarity SR2is greater than the second threshold TH2, it is considered that the second vibratory signature is known. If the second level of similarity SR′ is less than the second threshold TH2, it is considered that the second vibratory signature is not known and is indicative of an anomaly at the level of the machine. The second threshold TH2is for example set at 80%.

The monitoring phase F2comprises a step E7of generation of a warning when an anomaly is recognised. The warning may next be transmitted in the course of a transmission step E8. An operator may thus be warned that an anomaly at the level of the machine has been detected.

Apart from the sending of a warning, the monitoring phase F2may also comprise a step of transmission (not represented) of an analysis report of the vibrations of the machine. The analysis report comprises for example information relative to the frequency distribution and to the amplitude of the vibrations and in particular potential anomalies. The analysis report may also indicate the operating and stoppage times of the machine. The transmission of the analysis report is carried out regularly, for example every two hours.

In this preferential embodiment, the monitoring phase F2comprises a so-called “discovery” period which starts at the same time as the monitoring phase F2. Advantageously, the monitoring phase F2comprises a verification step E9carried out before generating the warning and during which it is verified if the second vibratory signal, corresponding to the second vibratory signature indicative of an anomaly, has been acquired during the discovery period. If this is not the case, the warning may be generated. If the second vibratory signal has indeed been acquired during the discovery period, the second vibratory signature is saved in the knowledge in the course of a saving step E5′ and the warning is not generated. In other words, it is considered that it is not really an anomaly but vibrations characteristic of the normal operation of the machine which did not show up during the learning phase F1. Thus, in the course of the discovery period, the knowledge may be enriched. The discovery period is particularly useful for machines having operating cycles that are variable over time. This is the case for example of air compressors, which have vibratory changes at the start and end of cycle. Preferably, the discovery period has a predetermined duration for example equal to seven days.

Advantageously, the monitoring phase F2comprises a step E10of comparison of the duration of the anomaly TAwith a predetermined duration T0. If the duration of the anomaly TAis greater than the predetermined duration T0, the warning is generated. In this case, it is in fact considered that the anomaly is proven. If the duration of the anomaly TAis less than the predetermined duration T0, the warning is not generated. This comparison step E9makes it possible to avoid triggering warnings in an inadvertent manner, for example when the machine suffers a one-off event, such as a shock, which could lead to a modification of the vibrations.

The steps of the monitoring phase F2are periodically repeated according to a second repetition period. Advantageously, the second repetition period is greater than the first repetition period in order to minimise electrical energy consumption and thus prolong the duration of operation of the monitoring device100. The second repetition period is for example equal to two minutes. The second repetition period may be dynamic, that is to say that it can vary, and in particular decrease, when an anomaly is detected.

An advantage is to enable a more precise characterisation of the anomaly, notably with regard to its duration.

Naturally, the invention is not limited to the embodiments described with reference to the figures and alternatives could be envisaged without going beyond the scope of the invention.