Method of monitoring a transmission assembly of a vehicle equipped with acceleration sensors, in particular a helicopter

Abstract: The method includes the steps of: acquiring a signal ( s(t) ) from an acceleration sensor; calculating the Hilbert transform (H[T' 1m (i)]) of the signal filtered and sampled beforehand; defining a complex signal ( C 1m (i) ) having the filtered and sampled signal ( T' 1m (i) ) as the real part and the Hilbert transform ( H[T' 1m (i)] ) of the filtered and sampled signal ( T' 1m (i) ) as the imaginary part; calculating a phase signal ( FC(i) ) given by the difference between the phase of the complex signal ( C 1m (i) ) and a reference phase ( P(i)+P 0 ); calculating the variance ( V ) of the phase signal ( FC(i) ); comparing the variance ( V ) with at least one predetermined threshold ( TH1 ); and generating an alarm signal if the variance ( V ) exceeds the threshold value ( TH1 ).

Description: The present invention relates to a method of monitoring a transmission assembly of a vehicle equipped with acceleration sensors, in particular a helicopter. As is known, helicopter transmission members must be monitored continuously to immediately detect any faults or incipient malfunction conditions, and so prevent failure during flight. For this purpose, the transmission is equipped with acceleration sensors, the signals of which are processed to determine any faults on the transmission. Examples for methods of monitoring transmission assemblies using signal processing techniques are shown in US 5 365 787 A or WO96/05486 A. The methods currently used, however, to process the fault detection sensor signals are not sensitive enough to ensure fault detection well in advance of catastrophic failure. It is an object of the present invention to provide a monitoring method ensuring reliable, advance detection of any faults or malfunction conditions which might result in failure. According to the present invention, there is provided a method of monitoring a transmission assembly of a vehicle equipped with acceleration sensors, in particular a helicopter, comprising the steps of: a) acquiring a signal from an acceleration sensor; b) calculating the Hilbert transform of said signal; c) defining a complex signal having said signal as the real part and said Hilbert transform of said signal as the imaginary part; d) calculating the difference between the phase of said complex signal and a reference phase to obtain a phase signal; e) calculating the variability of said phase signal to obtain a variability signal; and f) comparing said variability signal with at least a first predetermined threshold. A number of non-limiting embodiments of the invention will be described by way of example with reference to the accompanying drawings, in which: Figure 1 shows a top plan view of a helicopter, in which the helicopter transmission is shown schematically; Figure 2 shows a simplified diagram of the helicopter transmission and the location of various transmission member sensors; Figures 3-5 show flow charts of steps in the method according to the invention. Figures 1 and 2 show, schematically, the members of a helicopter 100 pertinent to the present invention. In particular, helicopter 100 comprises a first engine 101, a second engine 102, and a third engine 103; first engine 101 is connected, by means of a first transmission line 104 comprising a first, second and third reducer 105-107, to an input gear 108 of a main gear assembly 109; second engine 102 is connected to input gear 108 by means of a second transmission line 111 comprising a fourth, fifth and sixth reducer 112-114; and third engine 103 is connected to input gear 108 by means of a third transmission line 116 comprising a seventh, eighth and ninth reducer 117-119. Input gear 108 is connected to a rotor 121 of helicopter 100 by means of an epicyclic reducer 122 forming part of main gear assembly 109 and comprising six planet gears 123 and a sun gear 124; and input gear 108 is also connected - by means of a fourth transmission line 125 also connected to first transmission line 104 - to an accessory box indicated schematically by 126, and to a fifth transmission line 130 connected to a tail rotor 134 and comprising a power take-off gear 131, intermediate joints 135-137, an intermediate gearbox 139, and a tail gearbox 140. Figure 2 shows fifteen acceleration sensors 1-15 and two azimuth sensors 16, 17 fitted close to the reduction stages as indicated in Table I below. Sensor Pos. Sensor Pos. Sensor Pos. 1 105 7 131 13 140 2 112 8 122 (front) 14 136 3 117 9 122 (right) 15 137 4 107 10 122 (left) 16 108 5 114 11 126 17 139 6 119 12 139 Helicopter 100 also comprises a data processing unit 150 connected to sensors 1-17 by an interface unit 151 for sampling and digitizing the sensor signals, and to a data memory 152, an event memory 153, and a mathematical processor 154. The monitoring method described below provides, by analyzing the signals from acceleration sensors 1-7, 12 and 13, for detecting problems arising in flight, in particular, mechanical problems, torque transmission problems, structural problems involving cracks, and signal phase modulation problems; in all of which cases, at least one of the analyzed signals comprises a peak which is detectable by the method described. The monitoring method provides for processing a signal s(t), supplied by whichever of acceleration sensors 1-7, 12, 13 is considered in each case, using a signal s1(t) supplied by the azimuth sensor on a shaft connected to the reducer monitored by the sensor in question (azimuth sensor 16 for acceleration sensors 1-7, and azimuth sensor 17 for acceleration sensors 12, 13). Signal s(t) is a vibratory signal (related to the rotation frequency of the shaft associated with the sensor in question) and is affected by random noise as well as by noise related to other nearby rotary members. More specifically, and with reference to Figure 3, the method comprises an initial step, in which a variable F (explained later on with reference to Figure 5) is set to a first predetermined value, e.g. 0 (block 20). Signal s(t) and signal s1(t) are then acquired (block 21), and a sampling frequency FS is calculated by multiplying the frequency of azimuth sensor signal s1(t) by a memorized coefficient KT equal to the ratio between two prime whole numbers, and in particular correlated to the transmission ratio between the azimuth sensor shaft and the shaft monitored by the sensor in question, so that the resulting sampling frequency FS is correlated to the azimuth sensor frequency, and is such as to supply exactly NJ points (where NJ is a power of 2) of signal s(t) for each revolution of the monitored shaft. Signal s(t) is then sampled at frequency FS and filtered by interface unit 151 to remove the random noise and nonsynchronous periodic components (block 22) and obtain a filtered signal T 1m (i) defined by a number of samples indicated by "i". Signal s(t) is preferably sampled and filtered using the sequence of steps shown in Figure 4 and described later on. Signal T 1m (i) is further filtered using a band-pass filter centered on the gear meshing frequency, to obtain a filtered mean signal T' 1m (i) (block 24). The Hilbert transform H[T' 1m (i)] of signal T' 1m (i) is then calculated (block 25); and a complex signal C 1m (i) related to signal T' 1m (i) is calculated (block 26) according to the equation: C lm (i)=T' lm (i)+jH[T' lm (i)] The initial phase P 0 of signal C 1m (i) is then determined and memorized (block 27); and instantaneous phases P(i) are determined and memorized (block 28) according to the equation: P(i) = 2π Z NJ ·i i = 1... NJ where Z is the number of gear teeth. The phase FC(i) of complex signal C 1m (i) is then calculated (block 29) according to the equation: FC(i) =arg[C lm (i)]-P(i)-P 0 where arg[C lm (i)] is the argument of complex number C 1m (i) , defined as arctg{T' lm (i) / H[T' lm (i)]} , and P(i) and P 0 are the previously calculated phase values. The variance V of signal FC(i) is then calculated (block 30); the resulting value V is compared by central processing unit 150 with two threshold values TH1 , TH2 (where TH1 < TH2) to generate, if necessary, alarm signals (block 31) according to the procedure described in detail later on with reference to Figure 5; and block 31 then goes back to block 21 to continue monitoring with the next portion of signal s(t) . Sampling and filtering in block 22 are conveniently performed using the method described below with reference to Figure 4. To begin with, a revolution counter 1 is set to 1 (block 41); and signal s(t) is sampled at the previously defined frequency FS to obtain NJ points or samples T 1 (i) representing a synchronous vibratory time series relative to the sensor in question, to the respective shaft, and to each 1 -th revolution (block 42). The value of counter 1 , in particular whether it is less than or equal to a predetermined value K1 , is determined (block 43); and, if counter 1 is less than or equal to K1 (performance of fewer than K1 iterations corresponding to the revolutions of the monitored shaft - YES output of block 43), the availability of the signal is determined by calculating (block 47) the sum Δ d of the samples acquired at the 1 -th revolution, according to the equation: A limit value Δ 1 is then calculated (block 48) according to the equation: Δ 1 = K2 x FSV x NJ where FSV is the bottom-scale value, and K2 a predetermined constant much lower than 1; and a check is made (block 49) to determine whether the calculated sum Δ d is less than the limit value Δ 1 . In the event of a negative response (NO output of block 49), the signal is considered to exist, counter 1 is increased (block 50), and block 50 goes back to block 42 to acquire further NJ points relative to the next revolution. Conversely (YES output of block 49), the signal is considered nonexistent and an alarm signal is generated (block 51); a check is made (block 52) to determine the presence of a substitute sensor (e.g. sensor 6 for sensors 4 and 5); in the event of a positive response (YES output of block 52), block 52 goes back to block 21 in Figure 3 to repeat the procedure on the substitute sensor; and, in the absence of a substitute sensor, or if convergence is not reached even with the substitute sensor (NO output of block 52), the procedure is interrupted. Conversely, if counter 1 is greater than K1 (performance of more than K1 iterations corresponding to the revolutions of the monitored shaft - NO output of block 43), block 43 goes on to a block 55, which calculates the average contiguous synchronous time series T 1m (i) defining the filtered signal calculated in block 22, according to the equation: i = 1... NJ i.e. the mean value of each sample T 1 (i ) over the 1 revolutions considered is calculated. Convergence of the averaging process is then determined by calculating a convergence value Δ given by the sum, over all the samples, of the absolute value of the difference between the actual mean value of each sample and the mean value calculated in a previous revolution (at distance 4) divided by the sum of the mean samples in the previous revolution considered (at distance 4), according to the equation: where T 1m (i) represents the i -th sample of the 1 -th iteration, and T 1m-4 (i) represents the i -th sample of the 1-4 -th iteration (block 56). A check is then made to determine whether the calculated convergence value is less than or equal to a predetermined permissible minimum convergence value Δ c (block 57). In the event of a positive response (YES output), the convergence process is interrupted, and block 57 goes back to the main program (block 23 in Figure 3). Conversely, a check is made to determine whether the averaging process has already been performed a predetermined maximum number of times L (block 59). If the iteration (revolution) counter 1 is less than L (NO output), the counter is increased (block 50) and the operations described above are repeated. Conversely (YES output), the procedure for generating an alarm signal and possibly repeating the procedure with a substitute sensor, as described with reference to blocks 51-52, is repeated. The threshold comparison and alarm generating step in block 31 of Figure 3 is conveniently performed as described below with reference to Figure 5. To begin with, V is compared with first threshold TH1 (block 60); if V<TH1 (YES output), block 60 goes back to block 21 in Figure 3 to continue monitoring with the next group of samples; conversely, if threshold TH1 has been exceeded (NO output of block 60), the event is memorized in event memory 153 (block 62) and V is compared with second threshold TH2 (block 64). If V<TH2 (YES output of block 64), variable F (initialized in block 20 of Figure 3 to memorize whether threshold TH2 has already been exceeded) is set to (or confirmed at) the first predetermined value, 0 in the example shown (block 66), and block 66 goes back to block 21 of Figure 3 to continue monitoring with the next group of samples. Conversely (NO output of block 64), the event is memorized in event memory 153 (block 68) and the value of variable F is determined (block 70). In particular, if variable F is at the first predetermined value (YES output), F is set to a second predetermined value, e.g. 1, (block 72), and block 72 goes back to block 21 of Figure 3 to continue monitoring with the next group of samples. Conversely, if variable F is at the second predetermined value, i.e. threshold TH2 has already been exceeded (NO output of block 70), a pilot alarm signal is generated (block 74), variable F is again set to the first predetermined value (block 76), and block 76 goes back to block 21 of Figure 3 to continue monitoring with the next samples. The advantages of the method described are as follows. In particular, simultaneously controlling the various sensors installed provides for monitoring different structural parts of the helicopter instant by instant, and for detecting incipient mechanical problems caused by wear of the rotary parts of the transmission. Again at a structural level, the method described provides for detecting cracks in severely stressed parts of the helicopter, and which may result in rapid failure. Finally, at signal processing level, the method provides for detecting any problems arising in modulation of the sensor signals. Clearly, changes may be made to the method as described and illustrated herein without, however, departing from the scope of the present invention which is only limited by the wording of the appended claims. In particular, the filtering and averaging procedure and the alarm generating procedure may differ from those described.

Claims: A method of monitoring a transmission assembly of a vehicle equipped with acceleration sensors, in particular a helicopter, comprising the steps of: a) acquiring a signal ( s(t) ) from an acceleration sensor; b) calculating the Hilbert transform ( H[T' 1m (i)] ) of said signal; c) defining a complex signal ( C 1m (i) ) having said signal as the real part and said Hilbert transform of said signal as the imaginary part; d) calculating the difference between the phase of said complex signal and a reference phase to obtain a phase signal ( FC(i) ); e) calculating the variability of said phase signal to obtain a variability signal ( V ); and f) comparing said variability signal ( V ) with at least a first predetermined threshold ( TH1 , TH2). A method as claimed in claim 1, wherein said variability signal is the variance of said phase signal. A method as claimed in claims 1 or 2, wherein said steps a) to f) are repeated for a number of iterations. A method as claimed in any one of the foregoing claims, wherein said step of calculating said Hilbert transform is preceded by a step of sampling and filtering said signal. A method as claimed in claim 4, wherein said step of sampling and filtering comprises the steps of: acquiring a number of synchronous samples T 1 (i) for each revolution of a shaft associated with said acceleration sensor; and calculating an average time series T 1m (i) according to the equation: i = 1... NJ where T 1 (i) is said synchronous samples, 1 is a counter for counting the number of revolutions of said shaft, and N is the number of signal points. A method as claimed in claim 5, wherein said step of sampling and filtering also comprises the step of filtering said average time series ( T 1m (i) ) with a band-pass filter centered at a predetermined frequency, to obtain a filtered mean signal ( T' 1m (i) ). A method as claimed in claims 5 or 6, wherein said step of calculating an average time series is preceded by a step of determining the presence of significant signal values. A method as claimed in one of the foregoing claims from 5 to 7, wherein said step of calculating an average time series is followed by a step of determining convergence of said average time series. A method as claimed in any one of the foregoing claims, wherein said step of comparing said variability signal also comprises the step of comparing said variability signal with a second threshold.

Inventors: BELLAZZI ALBERTO, MAINO BRUNO, BELLAZZI, ALBERTO, MAINO, BRUNO
Applicants: AGUSTA SPA, AGUSTA S.P.A.
Application year: 1998
Main classification: G01M13
IPC codes: G01M13, B64C27, B64D45, F16H57, G01P15