System for reducing or optimising the electricity consumption or generation of a fluid device with at least one rotating component

Abstract

The invention relates to a system for reducing or optimising the electricity consumption or generation of a fluid device with at least one rotating component, the system comprising: - at least one electrical sensor (2) with electrodes (100) distributed around a body of the device, wherein their ends are flush with the inner surface of the body and face the component; - an electronic circuit (12) for controlling the electrodes and performing electrical impedance tomography measurements; - a signal processing unit suitable for reconstructing images of the fluid in the body from matrix measurements; - an electronic unit for automatically controlling the control member of the device suitable for modifying the operating frequency of the device so as to adjust its electrical power and, consequently, its flow-to-power ratio.

Description

[0001]Description Title: System for reducing or optimizing the electrical consumption or production of fluidic equipment with at least one rotating component. Technical field The present invention relates to the field of monitoring fluidic equipment intended to be implemented in a fluidic circuit, in particular in an industrial installation. The invention relates more particularly to the detection of air bubbles whose presence is due to a phenomenon known as cavitation, in a fluid flowing in a fluidic circuit and capable of generating excess electrical consumption or excess electrical production of fluidic equipment with a rotating component in said circuit. Although described with reference to a pump monitoring application, the invention can be applied to the monitoring of any type of fluidic equipment with a rotating component and / or capable of creating a pressure drop resulting in cavitation, such as a turbine,likely to be present in a fluid circuit, in particular in an industrial installation. By "fluidic equipment" is meant here and in the context of the invention, at least one rotating component or set of electromechanical components likely to be implemented in a fluid circuit, and adapted to be powered electrically or to produce electricity. It is an active device of which at least one rotating component moves volumes of fluid continuously and / or discontinuously (pump, marine propulsion) or uses the force of a fluid (turbine). Prior art Pumps are a key element of industrial installations, and are today, after electric motors, the second most manufactured device in the world. Pumps transport the fluids necessary for human activities, in particular industrial activities, such as most raw materials, water, fluids from the food industry, chemistry, pharmacy,construction, paper, electronics, mining, fossil fuels, etc. So-called cavitation phenomena, i.e. the presence of steam bubbles created by excessive suction power or by the absence of fluid at the suction of a pump, can cause excessive electricity consumption or even cause the pump to break. To solve this problem, the pumps can be dismantled and parts replaced or cleaned as part of scheduled maintenance. However, this can lead to unnecessary expenses because, to be safe, the pumps must be maintained more frequently than is actually necessary. There are a large number of fluid circuit monitoring systems integrating one or more pumps. They are mainly based on measurements of pressure differences, vibrations, infrared or ultrasonic measurements, as respectively described in patent applications / patents FR3026443A1,CN111197581A and EP1297331B1. These existing systems do not allow for real anticipation of over-consumption of electricity or, conversely, under-consumption of electricity, or even pump breakdowns. In particular, while some commercial solutions implemented allow detection of a high degree of cavitation, they can be complex to implement and are specific to certain types of pumps. There is therefore a need for a pump monitoring system in a fluid circuit that overcomes the drawbacks of known systems, in particular to allow cavitation to be detected simply and for all types of pumps, in order to optimize the electricity consumption of the pump and in any case to prevent pump breakdown that would be due to cavitation. More generally, there is a need for a system for monitoring fluidic equipment with at least one rotating component implemented in a fluid circuit,which is capable of detecting cavitation in a simple manner in order to optimize the electrical consumption or production of the equipment, and in any case, to prevent breakage of the equipment which would be due to cavitation. The aim of the invention is to meet at least partially these needs. Statement of the invention To do this, the invention relates, according to one of its aspects, to a system for reducing or optimizing the electrical consumption or electrical production of fluidic equipment with at least one rotating component, adapted to be electrically powered or to produce electricity, comprising: - at least one electrical sensor comprising a plurality of electrodes distributed around a body of the fluidic equipment, with their end flush with the internal surface of the body and facing the rotating component; - an electronic circuit for controlling the electrodes and for tomographic measurement by electrical impedance,the circuit being adapted to respectively simultaneously excite the electrodes, and measure electrical impedance matrices of the flowing fluid including, where appropriate, cavitation bubbles in the body; - a signal processing unit, adapted to reconstruct images of the flowing fluid in the body from the impedance matrix measurements of the electronic circuit; - an electronic unit for automatic control of the control member of the fluidic equipment, adapted to modify the operating frequency of the equipment so as to adjust its operating electrical power and thus adjust its flow / power ratio. According to an alternative embodiment, the fluidic equipment is a pump or a turbine, the end of the electrodes facing the free end of the blades of the pump or turbine or being integrated therein, the control member being a frequency converter. Advantageously,the operation of the electronic unit for automatic control of the fluidic equipment is controlled by that of the signal processing unit. According to an advantageous construction method, the system comprises a casing fixed around the body of the fluidic equipment, housing the electrical sensor with the electrodes housed and fixed individually in a sealed manner in a through hole of the body. Preferably, the casing houses a power supply port for the electrodes. According to an advantageous embodiment, the signal processing unit is adapted to implement a neural network for image reconstruction. According to an advantageous embodiment, the control circuit is adapted to implement the following steps: a / excitation of the electrodes, each electrode being excited by a potential V, n exc having either the form: [Math 2] where A is a signal amplitude, ^ ^the number of electrodes, θn is the angular position of electrode n, fm = m * f0 is an oscillation frequency, f0 is a fundamental frequency chosen such that f m is less than the Nyquist frequency of the system for all m, ^ the set of odd natural numbers, ^ the set of even natural numbers, or the form: [Math 5] ^ ^ ^^^ ^^^ in which, by convention, a sum is identically zero if the value of the starting index is greater than that of the final index, where: [Math 6] %-^^^ = ^ sin^2^^-^^ ,[Math 7] and [Math 8] and with denoting the (n- 1) th element of the identity defining the voltage ^ !^^^, under the convention that the terms are always arranged in ascending order of indices i in such an identity, the excitation being carried out either simultaneously on all the electrodes according to [Math 5], or sequentially on one or more subsets of electrodes; b / measurement of the electrical properties V n meas of the fluid flow with the electrodes, c / processing of the data from step b / of measurement, comprising the following sub-steps: c1 / for each electrode E n , calculation of data points M n defined by: [Math 10] where R is the value of the resistance used for the measurement of Vn meas with Vn meas = R In across the resistor, P is the number of points in a discrete sequence of measurements of the current I n , p is the discrete time, k is a Fourier coefficient between 1 and (n e– 1) and βp = (2πp / P), c2 / constitution of a data matrix D from the data points M n (k) for all n and for all k, according to the equation: [Math 13] if the excitation form [Math 2] is implemented, or [Math 14] if the excitation form [Math 5] is implemented, c3 / constitution of a signed data matrix whose elements are defined by the following equation when the phase shift Φn,l(k) between the excitation potential at electrode l and the current measured at electrode n is less than π / 2: [Math 20] and whose elements are defined by the following equation when the phase shift Φ n,l (k) between the excitation potential at electrode l and the current measured at electrode n is greater than or equal to π / 2: [Math 22] Q R ^ ^ = W ^ ^ Q ^ ^. where Σ is a sign matrix defined such that: - if the excitation form [Math 2] is implemented, the i-th element of the j-th row of Σ is the sign of cosine([2π / ([j+1] / 2)]*(i-1) / n e ) for odd j and the sign of sine([2π / (j / 2)]*(i-1) / ne) for even j, - if the excitation form [Math 5] is implemented, the opposite sign is assigned to the closest excitation electrode, in particular for a measuring electrode located at an equal distance between two excitation electrodes, the amplitude is experimentally measured to be of the order of O(10−7) A, corresponding to a negligible contribution to the data. Preferably, the driving circuit is adapted to implement step a / with the set of potentials Vn exc verifying the condition: [Math 4] According to an advantageous configuration, the electrodes of the electrical sensor are angularly distributed in a regular manner around the body of the fluidic equipment. The invention also relates to the use of the optimization system for measuring cavitation, air bubbles, the absence of fluid and the presence of gas. The invention also relates to an industrial installation, comprising at least one fluidic circuit and an optimization system as described above. In general, the invention can be implemented in any fluidic circuit, in particular in a factory or an industrial production site, in particular in the fields of food processing, pharmaceuticals and cosmetics, chemistry and petrochemistry, water distribution and treatment.The invention therefore essentially consists of a system for detecting cavitation likely to cause excess electrical consumption or excess electrical production or even breakage of fluidic equipment with a rotating component installed in a fluidic circuit. The system comprises an electrical sensor with electrodes distributed, preferably regularly around a body of the equipment and with their end flush with the internal wall of the body, preferably as close as possible to the blades of the rotating component. Electrical Impedance Tomography (EIT) measurements are carried out using either trigonometric signals or signals by pairs of electrodes, implemented simultaneously for the excitation of the electrodes.This measurement allows, in a non-invasive and non-destructive manner, to visualize, in real time and continuously, the interior of the body of the fluidic equipment and therefore the fluid flowing within it, by measuring the electrical properties (potential and electric current). The electrical impedance map inside the body is reconstructed, by solving the inverse problem. The acquisition speed of the images reconstructed by the processing unit can be very high, typically up to 31250 images / second, which makes it possible to observe fluids circulating at a flow rate of up to 300 meters / second. Depending on the signals of the flowing fluid representative of cavitation, an electronic unit will automatically modify the operating frequency of the equipment so as to adjust its operating electrical power and thus adjust its flow / power ratio.In this way, the risk of cavitation is eliminated, or at least reduced, and the electrical consumption or electrical production of the equipment is adapted to what is just necessary. The advantages of the invention which has just been described are numerous, among which we can cite: - direct measurement of cavitation causing excessive electrical consumption or even a pump failure; - rapid detection and possible preventive action due to the possibility of acquiring measurements / images at a very high rate, up to 31250 images / second; - a robust system because it is reliable whatever the fluid used, even under very high pressure, typically up to 300 bars or even beyond, and / or at very high temperatures, typically up to 600°C or even beyond.Brief description of the drawings [Fig 1] Figure 1 illustrates a fluid circuit comprising a pump, which can generate cavitation within the fluid flowing through the pump, this cavitation being able to be detected by an electrical consumption optimization system according to the invention, comprising an electrical sensor with electrodes for an electrical impedance tomography measurement. [Fig 2] Figure 2 schematically illustrates the hardware and software means of an optimization system according to the invention. [Fig 3] Figure 3 illustrates a pump body integrating an electrical sensor of a system according to the invention. [Fig 3A] Figure 3A is a cross-sectional view of a pump body according to Figure 3, at the electrodes of the electrical sensor. [Fig 4] Figure 4 illustrates spatial cosine patterns. [Fig 5] Figure 5 represents a part of an electronic circuit allowing the excitation of an electrode.[Fig 6] Figure 6 illustrates a method for generating excitation signals from the electrodes of the electrical sensor according to a first variant of the invention. [Fig 7] Figure 7 illustrates a method for measuring the signals generated by the electrodes. [Fig 8] Figure 8 represents in graphical form excitation signals and some of their properties. [Fig 9] Figure 9 illustrates a sign matrix for an electrical sensor with 16 electrodes. [Fig 10] Figure 10 illustrates a sign matrix for an electrical sensor with 32 electrodes. [Fig 11] Figure 11 illustrates images reconstructed respectively according to the state of the art and experimentally and simulated by neural network in a system according to the invention. Detailed description Figure 1 illustrates an example of implementation of a system 1 for optimizing the electrical consumption of a pump 20 moving a fluid in a circuit 2 comprising the pump 20.This system makes it possible to detect air bubbles due to cavitation which cause excessive electrical consumption or even breakage of the pump 20. This system also makes it possible, via impedance measurement, to measure dry running, foreign bodies, blockages, changes in viscosity or other types of defects which can reduce the performance of the pump or block it. The system can also measure in real time the internal wear of the pump and make a diagnosis of the hydraulic performance of the fluidic equipment. The system 1 firstly comprises an electrical sensor 10, arranged in a body 21 of the pump 2. This electrical sensor 10 makes it possible to carry out measurements by Electrical Impedance Tomography of the fluid flowing in this body 21.A unit 11 for operational control of the pump frequency converter, according to real-time measurements of faults so as to prevent the pump 20 from cavitation, according to the signals of the representative flowing fluid, as detailed below. More precisely, the electrical sensor 10 comprises a number of 16 electrodes 100 distributed, preferably angularly distributed in a regular manner, around the body 21 with their end flush with the internal surface of the body 21, preferably as close as possible to the end of the pump blades. The block diagram of the hardware and software means of the system 1 is illustrated in Figure 2.The electrodes 100 are connected to a printed circuit 12 which is an electronic circuit for controlling the electrodes 100 and for tomographic measurement by electrical impedance, the circuit being adapted to respectively excite the electrodes simultaneously, and to measure electrical impedance matrices of the flowing fluid including, where appropriate, cavitation bubbles in the body 21. A signal processing unit 13 makes it possible to reconstruct images of the flowing fluid in the body 21 from the impedance matrix measurements of the electronic circuit 12. An electronic automatic control unit 14 makes it possible, from a risk 13 identified or not for the pump 20, preferably by digital communication, to control the frequency converter of the pump, and more generally to carry out the supervision of the industrial installation in which the fluid circuit 2 is installed.By modifying the operating frequency of the pump 20, it is possible to adjust its operating electrical power and thus adjust its flow rate / power ratio. Figures 3 and 3A illustrate an advantageous mode of integration in the form of a casing 22 housing an electrical sensor 10 within a fluid circuit. A casing 22 fixed around the pump body 21, houses the electrical sensor 10 with the electrodes 100 housed and fixed individually in a sealed manner in a through hole 23 of the conduit. The casing 22 can house a power supply port 24 for the electrodes. The control circuit 12 can be connected to a data acquisition system. A screen 11 can be used to view the data and the images produced from this data. The data acquisition system contains a Linux operating system (HOST) which controls a programmable logic network FPGA, also contained in the data acquisition system.The acquisition system makes it possible to generate the analog excitation signals and to measure the analog measurement signals coming from the electrodes 100. The acquisition system can integrate, for example, a cRIO-9039 controller from the manufacturer National Instruments which includes a programmable logic network, NI-9262 modules from the manufacturer National Instruments for generating the analog excitation signals and NI-9223 modules from the manufacturer National Instruments for measuring the analog signals coming from the electrodes 100. The method of measuring and processing the signals implemented by the system for detecting faults and preventing breakdowns of the pump according to the invention will now be described. It is specified that beforehand the electrical sensor 10 is installed within the fluid circuit with the electrodes 100 arranged as indicated previously.Electrode excitation A first step consists of simultaneously exciting all the electrodes 100 by a potential having a well-chosen shape. The electrodes 100 constitute a set of linearly independent electrodes. They are used to create electrical excitations on the surface of the body and to measure its electrical properties. The excitation according to the variant of trigonometric patterns [Math 2] is as follows: for a number of n. e electrodes, there are (n e – 1) linearly independent excitation patterns. Fourier basis functions are a natural choice to describe these linearly independent patterns, according to the equation: [Math 1] According to a first alternative, all the electrodes are excited simultaneously using trigonometric excitations. This set of simultaneous excitations is decomposed into spatial oscillations according to the Fourier basis and into temporal oscillations. Different frequencies are imposed in order to distinguish between the different trigonometric signals in frequency multiplexing. For each trigonometric excitation pattern, each electrode En is associated with a static voltage V n sta . The set of n e static voltages V n sta forms a set of sine and cosine functions having different spatial frequencies m. Figure 4 shows the spatial cosine patterns for m ranging from 1 to 5. The sine patterns are not shown. The n eelectrodes are arranged on the periphery ∂Ω of the body, represented by the broken lines in Figure 4. The solid lines represent the excitation potential imposed on the electrodes. The static potential V n,m sta associated with each electrode E n is defined by the following equation, for n between 1 and ne: [Math 2] where A is a signal amplitude, m∈1,…,(n_e-1) represents the spatial frequency, ^ ^ the number of electrodes, θ n is the angular position of the electrode n, f m = m * f0is an oscillation frequency, f0is a fundamental frequency chosen such that f m is less than the Nyquist frequency of the system for all m, ^ the set of odd natural numbers, ^ the set of even natural numbers. For a given set of ne electrodes, all independent excitation patterns are fully described with (n e– 1) different spatial frequencies. For the particular case where θ n = 2π n / n e , the electrodes are distributed regularly around the periphery of the body. Each spatial frequency m is associated with a temporal frequency fm and is imposed simultaneously on each of the electrodes. Thus, each simultaneous excitation potential V n exc has an overlay of (n e – 1) trigonometric functions, each function oscillating at a particular frequency fm. The excitation signal of an electrode n is Vn exc , defined by the following equation: [Math 3] We simultaneously impose each potential V n exc (t) thus defined at each electrode En. We ensure that the sum of the excitation voltages of the nth electrodes is zero regardless of the time t. This results in the condition: [Math 4] According to a second alternative of the invention, the form of excitation is as follows: [Math 5] in which, by convention, a sum is identically zero if the value of the starting index is greater than that of the final index, where: [Math 6] %-^ ^ ^ = ^ sin ^ 2^^-^ ^ , [Math 7] And [Math 8] and with ^ ! ^ ^ ^ " ^#^ denoting the (n-1)th element of the identity defining the voltage under the convention that the terms are always arranged in ascending order of the indices i in such an identity. The excitation can be done on all the electrodes simultaneously or on a subset of electrodes sequentially. Measurement of electrical properties The electrical properties of the flowing fluid are then measured in section 21 using the electrodes 100. The unit 12 controls the programmable logic network of the acquisition system so as to generate 16 excitation signals having the properties described. These 16 digital signals are transformed into analog signals with the NI-9262 modules and transmitted by coaxial cables to the electrodes 100. The printed circuit 12 comprises an excitation circuit for each electrode 100, each of these circuits comprising a resistor R as illustrated in Figure 5. As visible in this figure, the potential Vn excis imposed on one side of the resistor R, the other side being connected to the electrode E n The Neumann boundary condition at electrode En is the current In flowing through the excitation circuit. This current is obtained by measuring the voltage Vn meas = R In across the resistor. As for V n exc , this signal is a sum of trigonometric functions. Data processing In a fourth step, the data measured during the second step are processed in order to either directly provide information to a processing algorithm or to obtain a signed data matrix representative of an image. Generation of data points Mn In a first sub-step of the fourth step of the measurement process, the data points M n are calculated for each electrode n. The Fourier transform of the measured signal Vn measis calculated from a P-point current measurement sequence In(p), where p is the discrete time and verifies 0 ≤ p ≤ P, i.e.: [Math 9] où d; = 2^= , i = √−1 and the normalization factor ^ = 1 . The Fourier transform can be calculated at the frequency f1 which corresponds to the frequency at which P Fourier coefficients are calculated. The frequencies fm of the voltages Vn exc are chosen to be harmonics of f1. This allows the measured signals to be distinguished. Thus, each coefficient k is associated with a particular frequency fm. The data are then generated at a frequency f1 and the resolution in Fourier space is Δf = f m+1 – f m = f1. It can be noted that the highest frequency is chosen to be lower than the Nyquist frequency of the system fNyq = 1 / 2 Δp, where Δp is the sampling time. The data points M n(k) are the moduli of each Fourier coefficient k for each electrode n: [Math 10] where R is the value of the resistance used for the measurement of V n meas with V n meas = RI n across the resistor, P is the number of points in a discrete sequence of current measurement In, p is the discrete time, k is a Fourier coefficient between 1 and (ne – 1) and β p = (2πp / P), Each data point defines the current of a given trigonometric pattern at a given electrode. The set of data points Mn(k) for all n and all k constitutes the measurement data. The excitation frequencies are determined as follows. The sampling frequency fDAQ of the data acquisition system limits the maximum frequency fm of the voltages Vn exec , the Nyquist frequency fNyq = fDAQ / 2 constituting an upper limit. For a data acquisition system such as f DAQ= 1 MS / s, the Nyquist frequency is equal to 500 kHz. To take advantage of the small errors in the residual voltage resulting from the energy stored in the electrode-electrolyte contact impedance, continuous signals must be provided. To generate continuous signals at different frequencies, the signals are chosen to be harmonics of the lowest generated frequency f1. When the potential is stopped on an electrode, part of the electrical energy is stored for a few tens of microseconds at the interface between the electrode and the medium. This contact impedance phenomenon causes an error in the measurement and implies the need to introduce a dead time between two successive excitations to wait for this energy to dissipate.The advantage of generating continuous signals is that the imposed voltages are never de-excited and therefore errors related to contact impedance and the need to introduce dead times are avoided. With 16 electrodes, a set of excitation signals is generated at 15 different frequencies (according to [Math 2]) and at 120 different frequencies (according to [Math 5]). Considering the sampling rate of the acquisition system, for example 1 MS / s, the frequencies can be chosen such that f. i = i * f0where f0is the fundamental frequency and i is between 1 and 15 or 1 and 120, respectively. Furthermore, in the case with 15 frequencies, the discrete Fourier transforms can be chosen to be done at P = 32 points because only positive results are considered. This results in an image data acquisition rate of 1 * 10 6 / 32 = 31250 frames per second. This choice implies that the lowest sinusoidal signal frequency f1 is equal to that of the discrete Fourier transform calculation frequency, and that the highest frequency f 15 = 15 * f0= 468.875 kHz, which is below the 500 kHz Nyquist limit of the system under consideration. The excitation amplitudes are determined as follows. The voltage generation and acquisition modules operate within a range of ± 10 V. Considering the voltages V n exc, the amplitude A of the sinusoids must be significantly lower than the sum of the generated sinusoids due to constructive interference. However, the amplitude A of the signal must be as large as possible to minimize the signal-to-noise ratio. Another limit to be taken into consideration is the maximum variation allowed between two successively generated potentials. Real-time control in the acquisition system makes it possible to choose a satisfactory value of A = 0.15 V, which gives resonance peaks at ± 2.25 V. The rapid transition between positive and negative values ​​of the signal prevents the occurrence of electrolytic effects. For example, an electrolysis phenomenon in water appears when a direct voltage greater than 1.2 V is imposed. This phenomenon does not appear for alternating voltages greater than 1.2 V if these voltages vary sufficiently rapidly.Obtaining a data matrix D In a second substep, a data matrix D is obtained from the data points M. n . For the considered 16-electrode system using NI-9223 modules, a fixed-point data format of 20 bits allocated to the number, including 5 bits for precision digits, can be used. The electrode index n, between 1 and 16, and the Fourier coefficient k, between 1 and 15, can be described by 4-bit binary numbers. For a 32-electrode system, each data point module M n (k) is encoded with the numbers n and k in the following form: [Math 11] 4 ^ ^5^ = ^ r + ss , t 8 s , s 0 u ^ + ^ r + ss , t 8 s , s 0 u ^ + ^ r± ss , s 1 t 6 s , s 1 s 1 u ^ , @ ^ x where the fixed point format<s, b, p> is used with s: signed / unsigned; b: number of allocated bits; p: number of bits for precision. The Mn(k) data is encoded in U32 format. The n e data points measured on the n e electrodes for a given Fourier coefficient k, that is, for a given frequency fm, give the following data vector: [Math 12] G 4^^5^H = ^ 4 ^ ^5^ 4 y ^5^ 4 z ^5^ … 4 ^^ ^5^ ^ where the M n (k) are integers encoded in U32 format. The ne – 1 vectors can be concatenated into a data matrix D: [Math 13] if the excitation form [Math 2] is implemented, or [Math 14] if the excitation form [Math 5] is implemented. The data size is then S = ne (ne – 1). Only the moduli of the Fourier transforms are part of the data. For an image, the data size is S * 32 bits = 4 kB. In comparison, the method described in publications [2], [3], [4] results in 127 kB of data for an image, without additional information on the boundary conditions. Thus, the measurement method implemented by the system according to the invention allows a higher image acquisition rate and also allows the data size to be reduced by a factor ne / 2 compared to the method described in publications [2], [3], [4]. Obtaining a signed data matrix In a third substep, a signed data matrix is ​​obtained, which is representative of an image. The Fourier transforms give a modulus and a phase. The sign of each data point is estimated from the phase.Thus, considering an excitation signal imposed on each electrode E. n at a frequency f m having the form: [Math 15] the phase of the signal is expressed: [Math 16] The phase of current I l meas (t) measured at electrode E l at frequency f m is: [Math 17] Assuming synchronous sampling between the analog signal input AI and the sampled signal output AO, the phase shift between the excitation potential and the measured current is: [Math 18] The phase shift depends on the design of the TIE sensors and the nature of the flow within the body. If the phase shift is large, wrapping effects may make it impossible to reconstruct the sign of the data. This is because the phase is symmetric to a 2πN transformation, where N is an integer. If the phase is greater than 2π, it is wrapped around itself. The following two cases have been identified: [Math 19] In the first case, the sign of the data points is calculated from the previous equation. The element of the signed data matrix corresponding to the k-th Fourier coefficient and the n-th electrode is then: [Math 20] In the second case, envelopment effects prevent the estimation of the sign of D n k. We then introduce a sign matrix Σ to allocate an arbitrary sign to the data. The sign matrix Σ is estimated from the sign of the excitation signal at t = 0 for a given harmonic at a given electrode by introducing the sign function: [Math 21] with Vn measas defined previously. The sign matrix Σ is more particularly defined so that its rows represent the signs of a cosine function alternating with those of a sine function having an integer number of periods on each row. The first two rows have a single period and the number of periods increases by one for each subsequent pair of rows. In other words, if the excitation form [Math 2] is implemented, the rows of the sign matrix Σ are defined such that the i-th element of the j-th row is the cosine sign ([2π / ([j+1] / 2)]*(i-1) / ne) for j odd and sine ([2π / (j / 2)]*(i-1) / ne) for j even. For example, the first element of the first row of the sign matrix is ​​the cosine sign(0), i.e. +. For a number of electrodes ne equal to 16, the sign of the fifth element of the first row is the cosine sign (2π*[4 / 16]), or 0.Thus, the first row of the sign matrix represents the signs of one period of a cosine function, that is, the signs of cosine (2π*(i-1) / n. e ) for the i-th element of the row. The second row represents the signs of one period of a sine function, i.e. the sine signs (2π*(i-1) / ne). The third row represents the signs of two periods of a cosine function, i.e. the cosine signs ([2π / 2]*(i-1) / n e ). The fourth line represents the signs of two periods of a sine function, i.e., the sine signs ([2π / 2]*(i-1) / ne). The fifth line represents the signs of three periods of a cosine function, i.e., the cosine signs ([2π / 3]*(i-1) / n e ), and so on until (n e-1)-th line. For example, for a 16-electrode device, the sign matrix takes the form shown in Figure 9. For a 32-electrode device, the sign matrix takes the form shown in Figure 10. If the excitation form [Math 5] is implemented, the opposite sign is assigned to the closest excitation electrode, especially for a measurement electrode located equidistant between two excitation electrodes. The amplitude is experimentally measured to be of the order of O(10−7) A, corresponding to a negligible contribution to the data. The use of such a sign matrix allows for the optimization of data processing to form an image. It allows the sign of each data point to be estimated and an image to be reconstructed.For a large phase shift, i.e. greater than or equal to π / 2, the arbitrarily signed amplitudes of the ne – 1 excitation patterns of the ne electrodes, in other words the elements of the signed data matrix corresponding to the k-th Fourier coefficient and the n-th electrode, are given by: [Math 22] Q. R ^ ^ = W ^ ^ Q ^ ^ . The HOST part of the acquisition system continuously sends the frequency and amplitude parameters to the FPGA of the acquisition system. Figure 6 illustrates an algorithm for generating the excitation signals for the 16 electrodes. With the system considered, the FPGA receives the data points 16 by 16 in a loop clocked at 1 MS / s to create 16 analog signals. In a first step, the system is initialized. Initially, the FPGA is empty. The HOST loads the FPGA and then the NI-9262 modules are reset. In a second step, an interrupt request is sent and received. A hardware interrupt is used to notify the HOST when the FPGA is ready to begin data acquisition. The FPGA waits for validation from the HOST to begin acquisition. In a third step, a sampling verification is performed. A sample pulse generation function is called to begin generating data points.The frequency at which the function is called determines the sampling rate for generating data points. In parallel, an I / O status write function is called at the same frequency to check the status of each generated sample. In a fourth step, digital excitation signal functions are generated. The HOST commands the FPGA to begin generating the excitation signal functions. In a fifth step, the analog excitation signals are generated. The sixteen excitation signals are sent to the electrodes. In a sixth step, the HOST confirms the generation of the signals and reports any errors at the HOST or FPGA level. Steps 1 and 2 are performed once at the start of the algorithm. Steps 3 to 6 are iterated for each output point at the sampling frequency. The sampling frequency can be between 10 kHz and 500 MHz, preferably between 500 kHz and 50 MHz.Figure 7 represents an algorithm for processing data received from the electrodes 100. The measurement of voltages across the resistors R allows the Neumann boundary conditions to be deduced for the implementation of the image reconstruction algorithm. The acquisition rate of 1 MS / s on 16 channels corresponds to a data transfer rate of 320 MB / s. The use of fast Fourier transforms, by considering only the Fourier coefficients related to a generated signal, allows the size of the data to be reduced without impacting their quality. This also acts as an effective band filter. However, the real-time calculation of 16 fast Fourier transforms requires a high computing capacity. The FPGA, which allows the real-time and parallel transformation of signals into their Fourier components on several channels, is a suitable tool for carrying out this task. In a first step A, the system is initialized.The FPGA resets the NI-9223 analog signal acquisition modules. In a second step (B), the memories are configured. The HOST configures and starts direct access to the FPGA memory. The FPGA configures and starts sixteen first-in-first-out queues to ensure communication between the sixteen measurement channels and their fast Fourier transform calculations. In a third step (C), an interrupt request is sent and received. A hardware interrupt ensures that the queues and direct access to memory are ready. In a fourth step (D), sampling is verified. A sample pulse generation function is called to control the sampling rate, and an I / O status read function is called at the same rate to check the status of each sample and report any errors to the HOST. In a fifth step (E), the analog measurement signals are acquired.The I / O read function is configured to read a single sample from each channel of each NI-9223 module. This function is called at 1 MHz and regulated by the sample pulse generation function. In a sixth step F, the fast Fourier transform of each channel is calculated in a loop at 1 MHz. The computation time is determined by the number P of points considered for the Fourier transform. Once the P measured data points are transferred for the Fourier transform calculations, the function restores the P Fourier coefficients one by one at each iteration of the Fourier transform loop. In a second step, the amplitudes of the Fourier coefficients are calculated at a frequency of 1 MHz. In a seventh step G, the data addressing is performed.The amplitude data points in U32 format form, together with the corresponding Fourier coefficient (in U16 format) and the corresponding channel (in U16 format), a data element in U64 format. At each iteration of the Fourier transform loop, 16 elements for the 16 channels are written to the random access memory to be transmitted to the HOST. In an eighth step H, the data matrix is ​​built. The HOST waits until the random access memory collects at least 240 elements, representing a complete data image. The address of the nth electrode and the Fourier coefficient associated with the amplitude are used to form the data matrix D. In a ninth step I, the data matrices are recorded. The data are used to perform real-time image reconstruction or to be recorded.The image is based on a one-step least square iterative reconstruction algorithm, for example the algorithm described in publication [5]. Real-time image reconstruction can generate on the order of a hundred images per second. In a tenth step J, signal acquisition is confirmed and error checking is performed. Synchronization of the analog signal generation and measurement modules is checked and possible errors are reported. Steps A to C are performed once at the algorithm start. Steps D to G are iterated for each output point at the sampling rate. Once a complete data matrix containing ne*(ne – 1) data points is acquired, the algorithm proceeds to step H. Steps H to J are iterated at the image acquisition rate. The sampling rate can be between 10 kHz and 500 MHz, preferably between 500 kHz and 50 MHz.Figure 8 shows, on the left-hand side graphs, the excitation signals for six of the sixteen electrodes. Each signal consists of a sum of 15 sinusoidal functions. The solid lines in the right-hand side graphs of Figure 8 represent, in equivalent Fourier space, the amplitudes of the measured voltages V. measat the level of the resistors R represented in figure 2, with R = 200 Ω. The broken lines represent the Fourier transforms of the voltages of the generated signals. Figure 11 illustrates the experimental images obtained by reconstruction by neural network from the aforementioned measurement matrices, of a flow of water comprising air bubbles, according to the invention and for comparison the experimental images according to the state of the art. It is clear that the detection according to the invention is much more precise than according to the state of the art and corresponds almost perfectly to the experimental situation. Depending on the signals of the flowing fluid representative of one or more cavitations, the automatic control electronic unit 14 modifies or not the operating frequency of pump 20 or even interrupts it, so as to avoid any excess electricity consumption and if necessary prevent any breakage of pump 20.The invention is not limited to the examples which have just been described; in particular, it is possible to combine characteristics of the illustrated examples within variants not illustrated. Other variants and improvements may be envisaged without departing from the scope of the invention. In particular, the method implemented by the system according to the invention may use acquisition systems other than that described. If in the illustrated example, the number of electrodes of the sensor is equal to 16 or 32, a smaller number, in particular 4 or 12 electrodes, or greater than 32, may be envisaged. List of references cited [1] Teague, G. (2002). Mass flow measurement of multi-phase mixtures by means of tomographic techniques. University of Cape Town, Faculty of Engineering, Department of Electrical Engineering. [2] Dupré, A., Mylvaganam, S. (2017). Simultaneous and Continuous Excitation Strategy for High Speed ​​EIT: the ONE-SHOT method.In Proceedings of the 9th World Congress on Industrial Process Tomography, pages 667 – 674. [3] Darnajou, M., Dupré, A., Dang, C., Ricciardi, G., Bourennane, S., Bellis, C. (2019). On the implementation of simultaneous multi-frequency excitations and measurements for electrical impedance tomography. Sensors, 19(17). [4] Darnajou, M., Dupré, A., Dang, C., Ricciardi, G., Bourennane, S., Bellis, C., Mylvaganam, S. (2020). High Speed EIT with Multifrequency Excitation using FPGA and Response Analysis using FDM. IEEE Sensors. [5] https: / / www.math.colostate.edu / ~siamcsu / files / NOSER.pdf.

Claims

Claims 1. System (1) for optimizing the electrical consumption or electrical production of fluidic equipment with at least one rotating component, adapted to be electrically powered or to produce electricity, comprising: - at least one electrical sensor (2) comprising a plurality of electrodes (100) distributed around a body of the fluidic equipment, with their end flush with the internal surface of the body and facing the rotating component; - an electronic circuit (12) for controlling the electrodes and for tomographic measurement by electrical impedance, the circuit being adapted to respectively excite the electrodes simultaneously, and to measure electrical impedance matrices of the flowing fluid including, where appropriate, cavitation bubbles in the body; - a signal processing unit (13),adapted to reconstruct images of the fluid flowing in the body from the measurements of impedance matrices of the electronic circuit; - an electronic unit (14) for automatic control of the control member of the fluidic equipment, adapted to modify the operating frequency of the equipment so as to adjust its operating electrical power and thus adjust its flow / power ratio.

2. Optimization system according to claim 1, the fluidic equipment being a pump or a turbine, the end of the electrodes facing the free end of the blades of the pump or the turbine or being integrated therein, the control member being a frequency converter.

3. Optimization system according to claim 1 or 2, the operation of the electronic unit for automatic control of the fluidic equipment being controlled by that of the signal processing unit.

4. Optimization system according to one of the preceding claims,comprising a casing fixed around the body of the fluidic equipment, housing the electrical sensor with the electrodes housed and individually fixed in a sealed manner in a through hole of the body.

5. Optimization system according to claim 4, the casing housing an electrical supply port for the electrodes., 6. Optimization system according to one of the preceding claims, the signal processing unit being adapted to implement a neural network for the reconstruction of the images.

7. Optimization system according to one of the preceding claims, the control circuit being adapted to implement the following steps: a / excitation of the electrodes, each electrode being excited by a potential Vn exc having either the form: [Math 2] where A is a signal amplitude, ^ ∈ 1, … , ^^ ^ − 1^represents the spatial frequency, ^ ^ the number of electrodes, θ nis the angular position of the electrode n, f m = m * f0 is an oscillation frequency, f0 is a fundamental frequency chosen such that fm is less than the Nyquist frequency of the system for all m, ^ the set of odd natural numbers, ^ the set of even natural numbers, or the form: [Math 5] in which, by convention, a sum is identically zero if the value of the starting index is greater than that of the final index, where: [Math 6] %-^^^ = ^ sin^2^^-^^ ,[Math 7] and [Math 8] 1^ ^3^ = ^ ^^ − ^^ + 1^ ^ 2 , and with denoting the (n- 1) th element of the identity defining the voltage ^ ! ^^^, under the convention that the terms are always arranged in ascending order of indices i in such an identity, the excitation being carried out either simultaneously on all the electrodes according to [Math 5], or sequentially on one or more subsets of electrodes; b / measurement of the electrical properties Vn meas of the fluid flow with the electrodes, c / processing of the data from step b / of measurement, comprising the following sub-steps: c1 / for each electrode En, calculation of the data points Mn defined by: [Math 10] where R is the value of the resistance used for the measurement of Vn meas with Vn meas = R In across the resistor, P is the number of points in a discrete sequence of measurements of the current I n , p is the discrete time, k is a Fourier coefficient between 1 and (n e – 1) and β p= (2πp / P), c2 / constitution of a data matrix D from the data points Mn(k) for all n and for all k, according to the equation: [Math 13] if the excitation form [Math 2] is implemented, or [Math 14] if the excitation form [Math 5] is implemented, c3 / constitution of a signed data matrix whose elements are defined by the following equation when the phase shift Φ n,l (k) between the excitation potential at electrode l and the current measured at electrode n is less than π / 2: [Math 20] and whose elements are defined by the following equation when the phase shift Φn,l(k) between the excitation potential at electrode l and the current measured at electrode n is greater than or equal to π / 2: [Math 22] Q R^ = ^ ^ ^ W^ Q^ .where Σ is a sign matrix defined such that: - if the excitation form [Math 2] is implemented, the i-th element of the j-th row of Σ is the sign of cosine([2π / ([j+1] / 2)]*(i-1) / n e ) for odd j and the sign of sine([2π / (j / 2)]*(i-1) / n e ) for even j, - if the excitation form [Math 5] is implemented, the opposite sign is assigned to the closest excitation electrode, in particular for a measurement electrode located at an equal distance between two excitation electrodes, the amplitude is experimentally measured to be of the order of O(10−7) A, corresponding to a negligible contribution to the data.

8. Optimization system according to claim 7, the control circuit being adapted to implement step a / with the set of potentials V n exc verifying the condition: [Math 4] 9. Optimization system according to one of the preceding claims, the electrodes of the electrical sensor being angularly distributed regularly around the body of the fluidic equipment.

10. Use of the optimization system according to one of the preceding claims for measuring cavitation, air bubbles, the absence of fluid and the presence of gas.

11. Industrial installation, comprising at least one fluidic circuit and an optimization system according to one of claims 1 to 9.

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