JP4513710B2 - Abnormality diagnosis device - Google Patents

Description

  The present invention relates to an abnormality diagnosis apparatus for rotating or sliding parts used in railway vehicles, automobiles, windmills, and the like, and more particularly, to an abnormality diagnosis apparatus for specifying presence / absence or a sign of an abnormality of the part or an abnormal part thereof.

  Conventionally, after rotating parts of a railway vehicle are used for a certain period, the axle bearings and other rotating parts are regularly inspected for abnormalities such as damage and wear. This periodic inspection is performed by disassembling the mechanical device in which the rotating part is incorporated, and the operator can detect damage and wear generated in the rotating part by visual inspection. And the main defects found in the inspection are in the case of bearings, indentations caused by the biting of foreign matter, peeling due to rolling fatigue, other wear, etc., in the case of gears, missing teeth and wear, etc. In the case of a wheel, there is wear such as a flat, and in any case, if irregularities or wear that is not found in a new article is found, it is replaced with a new one.

  However, in the method of disassembling the entire mechanical equipment and visually inspecting by the operator, disassembly work to remove the rotating body and sliding member from the device, and reassembly of the inspected rotating body and sliding member into the device There has been a problem that a great deal of labor is required for the work, and the maintenance cost of the apparatus is greatly increased.

  Further, when reassembling, the inspection itself may cause a defect in the rotating body or the sliding member, such as attaching a dent to the rotating body or the sliding member that was not present before the inspection. Further, since a large number of bearings are visually inspected within a limited time, there is a problem that a possibility of overlooking a defect remains. Further, the determination of the degree of the defect also varies depending on the individual, and parts are exchanged even if there is substantially no defect, resulting in a wasteful cost.

  Accordingly, various methods have been proposed for diagnosing abnormalities of rotating parts in an actual operating state without disassembling a mechanical device in which the rotating parts are incorporated (see, for example, Patent Documents 1 to 3). Most commonly, as described in Patent Document 1, an accelerometer is installed in the bearing section, the vibration acceleration of the bearing section is measured, and FFT (Fast Fourier Transform) processing is performed on this signal. There is known a method of performing diagnosis by extracting a signal of vibration generation frequency components.

In addition, various methods have been proposed for detecting a flat portion called a flat caused by friction and wear on the rolling surface of a railway vehicle wheel due to a brake malfunction or sliding with a rail due to malfunction of the brake (for example, patents) References 4-6.) In particular, Patent Document 4 proposes a device that detects a defect state of a railroad vehicle wheel and a track through which the train passes by a vibration sensor, a rotation measuring device, or the like.
JP 2002-22617 A JP 2003-202276 A Japanese Patent Laid-Open No. 2004-257836 Japanese National Patent Publication No. 9-500452 JP-A-4-148839 Special table 2003-535755 gazette

In rotating machines and vehicles, machine abnormalities can be diagnosed based on the magnitude of a specific frequency component of vibration proportional to the rotational speed and moving speed of the components contained in them. Diagnosis is frequently used. At that time, the output signal of the vibration sensor is digitized by AD conversion, and digital arithmetic processing mainly including FFT processing is performed. The AD conversion is normally performed using an AD converter of a dedicated IC or an AD converter built in a microcomputer. The use of a high-resolution and high-precision AD converter for a dedicated IC increases the cost. On the other hand, if an AD converter with a built-in microcomputer is used, the resolution is low but the cost is low and the space can be saved. Even when using a microcomputer that does not include an AD converter, if a low-resolution AD converter is used or the AD converter can be omitted, the cost and space can be reduced.
However, if the resolution of AD conversion is lowered, the dynamic range of the signal is lowered, so that the frequency peak detection accuracy by FFT processing is also lowered. If an automatic gain control (AGC) circuit or the like for amplifying a signal to an appropriate amplitude is provided in order to avoid a decrease in dynamic range, the cost is increased.

  The present invention has been made in view of the above-described circumstances, and its purpose is to use a low-resolution AD converter or a simple comparator to convert an analog signal from a vibration sensor into a digital signal. An object of the present invention is to provide an abnormality diagnosing device that can reduce the cost and save space, and can perform an abnormality diagnosis without causing a decrease in accuracy.

The object of the present invention is achieved by the following constitution.
(1) An abnormality diagnosis device for a mechanical device having rotating or sliding parts,
An AD converter that converts an analog signal from a vibration sensor that detects vibration of the mechanical device into a digital signal;
A diagnostic processing unit that performs a Fourier transform process on the digital signal from the AD converter and performs an abnormality diagnosis based on the result;
With
The diagnosis processing unit
An abnormality diagnosing apparatus configured to perform a Fourier transform process on a digital signal from the AD converter by extending a data width beyond the resolution of the AD converter.
(2) An abnormality diagnosis device for a mechanical device having rotating or sliding parts,
An AD converter that converts an analog signal from a vibration sensor that detects vibration of the mechanical device into a digital signal;
A diagnostic processing unit that performs a Fourier transform on the digital signal from the AD converter and performs an abnormality diagnosis based on the result;
With
The diagnosis processing unit
An abnormality diagnosing apparatus, wherein the AD converter has a resolution of 1 bit and is expanded to a predetermined data width of 2 bits or more to perform Fourier transform processing.
(3) An abnormality diagnosis device for a mechanical device having rotating or sliding parts,
A comparator that compares a voltage of an analog signal from a vibration sensor that detects vibration of the mechanical device with a reference voltage and outputs a binary signal indicating whether the voltage of the analog signal is higher or lower than the reference voltage; Prepared,
The diagnosis processing unit
An abnormality diagnosing apparatus configured to perform Fourier transform processing by extending a signal from the comparator to a predetermined data width.

  According to the abnormality diagnosis device of the present invention, low-resolution AD converters and simple comparators can be used to reduce the cost and space of the circuit, and to perform abnormality diagnosis without causing a decrease in accuracy. .

  Hereinafter, a first embodiment, a second embodiment, and a third embodiment of an abnormality diagnosis apparatus according to the present invention will be described in detail with reference to the drawings.

  1A is a schematic plan view of a railway vehicle equipped with the abnormality diagnosis device of the present invention, FIG. 1B is a schematic side view of the railway vehicle, and FIG. 2 illustrates the positional relationship between an axle bearing and a vibration sensor. FIG. 3 is a block diagram of the first embodiment of the abnormality diagnosis apparatus according to the present invention, FIG. 4 is a flowchart showing the operation contents of the diagnosis processing unit of FIG. 3, and FIG. FIG. 5B is an explanatory diagram showing an example of mere sign extension of the digital signal from the AD converter, FIG. 6 is a scratched part of the axle bearing, and FIG. FIG. 7 is a diagram showing a relationship of vibration generation frequencies generated due to scratches, FIG. 7 is a block diagram of a main part of a second embodiment of the abnormality diagnosis apparatus according to the present invention, and FIG. 8A is an analog from a vibration sensor. About processing to convert signal to binary signal by comparator FIG. 8B is a waveform diagram after the signal from the comparator is digital-filtered by the microcomputer in the diagnostic processing unit, FIG. 9 is a flowchart showing the operation content of the diagnostic processing unit in the second embodiment, and FIG. 10 is a principal block diagram of a third embodiment of the abnormality diagnosis apparatus according to the present invention.

[First Embodiment]
First, with reference to FIGS. 1-6, the abnormality diagnosis apparatus of 1st Embodiment is demonstrated.
As shown in FIG. 1, one rail vehicle 100 is supported by two front and rear chassis, and four wheels 101 are attached to each chassis. A vibration sensor 111 that detects vibration generated from the rotation support device 110 during operation is attached to the rotation support device (bearing box) 110 of each wheel 101.

  The control panel 115 of the railway vehicle 100 is equipped with two abnormality diagnosis devices 150 that take in sensor signals for four channels simultaneously (substantially simultaneously) and perform diagnosis processing. In other words, the output signals of the four vibration sensors 111 provided in each chassis are input to another abnormality diagnosis device 150 for each chassis via the signal lines 116. The abnormality diagnosis device 150 also receives a rotation speed pulse signal from a rotation speed sensor (not shown) that detects the rotation speed of the wheel 101.

  As shown in FIG. 2, the rotation support device 110 is provided with an axle bearing 130 that is a rotating part as an example, and the axle bearing 130 is a rotating wheel that is externally fitted to a rotation shaft (not shown). An inner ring 131, an outer ring 132 that is a fixed ring fitted in a housing (not shown), a ball 133 that is a plurality of rolling elements disposed between the inner ring 131 and the outer ring 132, and the ball 133 can freely roll. And a retainer (not shown). The vibration sensor 111 is held in a posture capable of detecting vibration acceleration in the direction of gravity and is fixed near the outer ring 132 of the housing. Various sensors such as an acceleration sensor, an AE (acoustic emission) sensor, an ultrasonic sensor, and a shock pulse sensor can be used as the vibration sensor 111.

  As shown in FIG. 3, the abnormality diagnosis apparatus 150 includes a sensor signal processing unit 150A and a diagnosis processing unit (MPU: Micro Processing Unit) 150B. The sensor signal processing unit 150A includes one amplifier (Amp) 171 and one filter (LPF) 172 for one vibration sensor 111 {that is, four amplifiers (Amp) 171 and four filters. Device (LPF) 172}. The output signals (analog signals) of the four vibration sensors 111 are respectively input to the corresponding amplifiers (Amp) 171 and amplified, and then input to the corresponding filter (LPF) 172. Yes. The analog signal amplified and filtered by the amplifier (Amp) 171 and the filter (LPF) 172 is taken into the diagnostic processing unit (MPU) 150B, and the AD in the diagnostic processing unit (MPU) 150B is passed through the multiplexer (MUX) 152. A converter (ADC) 153 converts the signal into a digital signal. On the other hand, the rotational speed pulse signal from the rotational speed sensor is shaped by the waveform shaping circuit 155, and then taken into the diagnostic processing unit (MPU) 150B, and is received by the timer counter (TCNT) 154 in the diagnostic processing unit (MPU) 150B. The number of pulses per unit time is counted, and the value is processed as a rotation speed signal. The diagnosis processing unit (MPU) 150B performs abnormality diagnosis based on the vibration waveform detected by the vibration sensor 111 and the rotation speed signal detected by the rotation speed sensor. The diagnosis result by the diagnosis processing unit (MPU) 150B is output to the communication line 120 (see also FIG. 1) via the line driver (LD) 160. The communication line 120 is connected to an alarm device so that an appropriate alarm operation is performed when an abnormality occurs.

  Abnormalities that can be detected from the output signal of the vibration sensor 111 are peeling of the axle bearing 130 and flatness (wear) of the wheel 101. Here, diagnosis of the axle bearing 130 will be described. Among the abnormalities occurring in the axle bearing 130, the outer ring raceway of the stationary wheel is most likely to be peeled off. Therefore, the separation of the outer ring raceway of the stationary wheel of the axle bearing 130 is targeted for detection.

  In this embodiment, an amplifier (Amp) 171 and a filter (LPF) 172 are used to amplify and filter the output signal of the vibration sensor 111. The data filtered by the filter (LPF) 172 and converted into a digital signal by the AD converter (ADC) 153 is processed by an arithmetic function realized by software, and abnormality diagnosis based on the output signal of each vibration sensor 111 is performed. Do. The diagnostic processing unit (MPU) 150B includes a memory (RAM) 159 therein, and can use this to execute FFT and digital filtering at extremely high speed. Thus, real-time processing (that is, calculation in a short time with a margin much longer than the sampling time) can be performed on the vibration sensor 111 of four channels.

  The output signal of the vibration sensor 111 is input to an AD converter (ADC) 153 in the diagnostic processing unit (MPU) 150B through an amplifier (Amp) 171 and a filter (LPF) 172. The resolution of the AD converter (ADC) 153 in this embodiment is 8 bits. The diagnostic processing unit (MPU) 150B reads the vibration data as an 8-bit value. Further, in order to keep the sampling frequency of the AD converter (ADC) 153 constant and suppress the load on the CPU 158, a compare match timer (CMT) 156 and a direct memory access controller (DMAC) 157 are used. The sampling frequency is 8 kHz. The filter (LPF) 172 also functions as an anti-aliasing filter and reduces band components of 1 kHz or more.

  The input range of the AD converter (ADC) 153 is 0 to 3.3V. The vibration sensor 111, the amplifier (Amp) 171 and the filter (LPF) 172 are designed so that the vibration waveform is adapted to the input range of the AD converter (ADC) 153 and the center voltage of the vibration waveform is 1.65V. Has been.

  FIG. 4 shows an operation flow of the diagnostic processing unit (MPU) 150B. The diagnostic processing unit (MPU) 150B outputs each of the sensor signals output from the four vibration sensors 111 and sent through the amplifier (Amp) 171 and the filter (LPF) 172 via the multiplexer (MUX) 152. By sampling while switching channels, the multi-channel is sampled almost simultaneously by the AD converter (ADC) 153 and converted into a digital signal (unsigned 8-bit data) (ie, step S101).

  The unsigned 8-bit data output from the AD converter (ADC) 153 is first converted into signed 16-bit data (ie, step S102). Specifically, as shown in FIG. 5 (a), after re-encoding 8-bit data so that 1.65V that is the center voltage of the vibration waveform becomes 0V, 8 bits are added to the lower part thereof. Convert to a 16-bit value.

  Next, fixed-point digital filter processing (that is, step S103) is performed, envelope (absolute value) processing (that is, step S104) is performed, and then 16-bit fixed-point FFT processing (that is, step S105) is performed. . Then, the frequency peak is obtained from the result of the FFT process (ie, step S105) (ie, step S106). Further, a bearing defect frequency is calculated from the axle rotation speed and the bearing specifications (see FIG. 6) (ie, step S107). Then, the degree of coincidence between the frequency peak and the bearing defect frequency is digitized (that is, step S108), and abnormality (NG) is determined from the accumulated value of a predetermined number of times (that is, step S109).

In the fixed-point operation from the 16-bit fixed-point digital filter processing (ie, step S103) to the 16-bit fixed-point FFT processing (ie, step S105), the lower 15 bits of the 16 bits are used for the expression below the decimal point. The coefficient of the digital filter is −1.0 or more and less than 1.0 when expressed as a real number. If this fixed-point number expression is used, it becomes −2 ¹⁵ or more and 2 ¹⁵ −1 or less in the computer. If it is 8 bits, it is -2 ⁷ or more and 2 ⁷ -1 or less in the case of being signed. Since the filter processing reduces the amplitude of the waveform, the data with a width of 8 bits becomes data with a smaller amplitude, which hinders the accuracy of frequency peak detection. Therefore, the amplitude range of AD conversion is set to a real number of −1.0 or more and less than 1.0, and matched with the data width of the CPU 158. To convert signed 8-bit data to signed 16-bit data, the most significant bit and 7 bits after the decimal point of the signed 8-bit data are left as the upper 8 bits, and the lower 8 bits are all set to 0. do it. In short, the integer in the range of −128 to 127 is enlarged 256 times, converted to an integer in the range of −32768 to 32767, and the calculation proceeds. On the other hand, as shown in FIG. 5 (b), even if it is expanded to 16 bits, it is merely code-extended, and there is no effect unless it is expanded.

  The FFT process (ie, step S105) was performed by fixed point arithmetic of 16-bit data. The reason is that since the CPU 158 used is a 32-bit CPU, the 16-bit × 16-bit multiplication is prevented from overflowing, and the floating-point arithmetic unit (FPU) is not provided, so that the floating-point number is not used. This is because it is desirable in terms of speed.

  In the FFT process (that is, step S105), the scaling process is performed. That is, when FFT is performed with the number of calculation points being 2 to the Nth power, N stages of butterfly calculation are performed. At this time, data is reduced to prevent overflow.

  In this way, in fixed point arithmetic, the dynamic range tends to be small due to the bit width limitation. Furthermore, if the input data is half of 8 bits, the abnormal signal is buried in the calculation error, and the probability that the detection of the vibration peak will not be successful becomes very high. Therefore, in the present embodiment, the peak to be detected is prevented from disappearing by calculating the 8-bit data by expanding it to 16 bits in advance.

  In this abnormality diagnosis processing, frequency analysis and its peak detection are important, and it is not required to faithfully sample and restore the original waveform. Therefore, even if the initial AD conversion data is as small as 8 bits, as described above, By enlarging, the characteristics of frequency can be captured sufficiently.

As one verification example, Table 1 shows the result of an attempt to detect the separation of a tapered roller bearing for a railway vehicle together with a comparative example.

  Abnormal vibration 1 is a vibration signal when the bearing with the outer ring raceway surface of the bearing separated is rotating at 240 rpm. Abnormal vibration 2 is a vibration signal when a bearing having an artificial defect formed by electric discharge machining on the outer ring raceway surface of the bearing is rotating at 360 rpm. Abnormal vibration 3 is a vibration signal when a bearing in which an artificial defect is formed on the outer ring raceway surface of the bearing is rotated at 990 rpm.

  In any case of abnormal vibration, when the 16-bit integer value obtained from the 16-bit AD converter was used for the calculation as it was, the abnormality was successfully detected. On the other hand, when an arithmetic operation was performed by performing only sign extension with the 8-bit integer value obtained from the 8-bit AD converter, an abnormality could not be detected. On the other hand, when the 8-bit integer value obtained from the AD converter was expanded to 16 bits after encoding and the operation was substantially expanded to 256 times, the abnormality was successfully detected.

  As described above, the output signal from the AD converter (ADC) 153 that converts the analog signal from the vibration sensor 111 into a digital signal is a data width larger than the resolution (8 bits in this example) of the AD converter (ADC) 153. Is expanded (expanded to 16 bits in this example), Fourier transform processing is performed, and abnormality diagnosis is performed based on the result, thereby reducing the cost and space of the circuit by using a low-resolution AD converter. Abnormal diagnosis can be performed without causing a reduction in accuracy.

[Second Embodiment]
FIG. 7 is a principal block diagram of a second embodiment of the abnormality diagnosis apparatus according to the present invention. This embodiment shows an example in which a microcomputer system (that is, a microcomputer system) that does not include an AD converter is used. An analog signal (waveform signal) from the vibration sensor 111 is amplified by an amplifier (Amp) 171. Immediately after passing through the filter (LPF) 172, it is input to the port (Port) of the diagnostic processing unit (MPU) 150B via the comparator 173. That is, in this embodiment, instead of the diagnosis processing unit (MPU) 150B not having the AD converter 153, the sensor signal processing unit 150A is provided with a comparator 173. Other configurations are the same as those of the first embodiment.

  As the comparator 173, a hysteresis comparator is used to eliminate the influence of noise. The comparator 173 compares the voltage of the analog signal (see the waveform in the upper part of FIG. 8A) from the vibration sensor 111 with a constant reference voltage ref, and determines whether the voltage of the analog signal is higher than the reference voltage ref. A 1-bit signal indicating the low level (see the waveform at the bottom of FIG. 8A) is output. The reference voltage ref is, for example, the center voltage (1.65 V) of the vibration waveform. The sampling frequency of the comparator 173 is 32 kHz. The 1-bit (ie, binary) signal from the comparator 173 input to the port (Port) of the diagnostic processing unit (MPU) 150B is digitally filtered in the diagnostic processing unit (MPU) 150B. The signal has the waveform shown in FIG.

  FIG. 9 shows an operation flow of the diagnostic processing unit (MPU) 150B in the second embodiment. The diagnostic processing unit (MPU) 150B receives a signal from the comparator 173 (ie, step S201). The value of the port of the diagnostic processing unit (MPU) 150B takes only 0 and 1. Since this corresponds to the sign bit in AD conversion, it is simply positive or negative, that is, 0 represents -1 and 1 represents 1. Therefore, it is converted into signed 16-bit data (ie, step S202). The calculation starts from a binary value of -32768 and 32767 with a signed 16-bit integer.

  Next, FIR digital filter processing (that is, step S203) is performed, envelope (absolute value) processing (that is, step S204) is performed, and then 16-bit fixed point FFT processing (that is, step S205) is performed. Then, the frequency peak is obtained from the result of the FFT process (ie, step S205) (ie, step S206). Further, a bearing defect frequency is calculated from the axle rotation speed and the bearing specifications (see FIG. 6) (ie, step S207). Then, the degree of coincidence between the frequency peak and the bearing defect frequency is digitized (that is, step S208), and abnormality (NG) is determined from the accumulated value of a predetermined number of times (that is, step S209).

  The defect frequency of the axle bearing 130 is targeted at 1 kHz or less, but the vibrations generated from the bearing member, sensor case, etc. include many vibrations having a frequency higher than 1 kHz. The propagation of the vibration detected by the vibration sensor 111 is performed by the vibration of these members, and it can be considered that the low-frequency vibration frequency due to the defect modulates the high-frequency vibration (carrier wave). Therefore, in this embodiment, the sampling frequency of the comparator 173 is set as high as 32 kHz. By increasing the sampling frequency, a low defect frequency can be recovered even with binary data. The principle is the same as that of PWM {Pulse Width Modulation}. The FIR low-pass filter process (that is, step S203) is performed in order to narrow the waveform signal in the range of the defect frequency, excluding the carrier component.

  As described above, even when the AD converter is not used and the lower cost comparator 173 is used, the binary data output from the comparator 173 is expanded to 16-bit width data to perform an arithmetic process. It is possible to perform frequency analysis by FFT processing sufficient to detect signal peaks.

[Third Embodiment]
FIG. 10 is a principal block diagram of a third embodiment of the abnormality diagnosis apparatus according to the present invention. Similar to the second embodiment, instead of the diagnosis processing unit (MPU) 150B having the AD converter 153, the sensor signal processing unit 150A is provided with a comparator 173. In the second embodiment, the reference voltage ref is constant, but in this embodiment, a sine wave having a higher frequency than the analog signal from the vibration sensor 111 is used as the reference voltage ref. The comparator 173 samples and digitizes (binarizes) the analog signal from the vibration sensor 111 at a frequency higher than the reference voltage ref.

  The diagnostic processing unit (MPU) 150B digitally performs a low-pass filter process on the binary signal from the comparator 173, thereby realizing the function of the multi-bit AD converter in software. In the second embodiment described above, the order of the characteristic frequency of bearing separation is 1 kHz at most, but a high frequency component due to the natural vibration of the bearing ring, rolling element or vibration sensor 111 of the bearing 130 is superimposed on the vibration waveform. Since the low-pass filter processing is performed by the software of the diagnostic processing unit (MPU) 150B, the processing equivalent to that of the third embodiment is performed as a whole. However, it can be said that the second embodiment is more advantageous in terms of cost in that the sine wave generation circuit is unnecessary.

  In the above embodiment, the case where the abnormality diagnosis of the axle bearing 130 is performed has been described. However, the abnormality diagnosis device of the present invention can also be effectively applied to abnormality diagnosis of wheels and other mechanical devices.

(A) It is a schematic plan view of a railway vehicle equipped with the abnormality diagnosis device of the present invention, and (b) is a schematic side view of the railway vehicle. It is the schematic which illustrates the positional relationship of an axle shaft bearing and a vibration sensor. 1 is a block diagram of a first embodiment of an abnormality diagnosis apparatus according to the present invention. It is a flowchart which shows the operation | movement content of the diagnostic process part of FIG. (A) is explanatory drawing about the process which expands the digital signal from AD converter rather than the resolution | decomposability, (b) is explanatory drawing which shows the example of the mere code extension of the digital signal from AD converter. It is a figure which shows the relationship between the site | part of the damage of an axle shaft bearing, and the vibration generation frequency which arises resulting from a damage | wound by a numerical formula. It is a principal part block diagram of 2nd Embodiment of the abnormality diagnosis apparatus which concerns on this invention. (A) is explanatory drawing about the process which converts the analog signal from a vibration sensor into a binary signal with a comparator, (b) is the waveform figure after digital-filtering the signal from a comparator with the microcomputer in a diagnostic process part It is. It is a flowchart which shows the operation | movement content of the diagnostic process part in 2nd Embodiment. It is a principal part block diagram of 3rd Embodiment of the abnormality diagnosis apparatus which concerns on this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 100 Rail vehicle 101 Wheel 110 Rotation support apparatus 111 Vibration sensor 130 Axle bearing 150 Abnormality diagnosis apparatus 150A Sensor signal processing part 150B Diagnosis processing part 153 AD converter 171 Amplifier 172 Filter 173 Comparator

Claims (3)

An abnormality diagnosis device for a mechanical device having a rotating or sliding part,
An AD converter that converts an analog signal from a vibration sensor that detects vibration of the mechanical device into a digital signal;
A diagnostic processing unit that performs a Fourier transform process on the digital signal from the AD converter and performs an abnormality diagnosis based on the result;
With
The diagnosis processing unit
An abnormality diagnosing apparatus configured to perform a Fourier transform process on a digital signal from the AD converter by extending a data width beyond the resolution of the AD converter. An abnormality diagnosis device for a mechanical device having a rotating or sliding part,
An AD converter that converts an analog signal from a vibration sensor that detects vibration of the mechanical device into a digital signal;
A diagnostic processing unit that performs a Fourier transform process on the digital signal from the AD converter and performs an abnormality diagnosis based on the result;
With
The diagnosis processing unit
An abnormality diagnosing apparatus, wherein the AD converter has a resolution of 1 bit and is expanded to a predetermined data width of 2 bits or more to perform Fourier transform processing. An abnormality diagnosis device for a mechanical device having a rotating or sliding part,
A comparator that compares a voltage of an analog signal from a vibration sensor that detects vibration of the mechanical device with a reference voltage, and outputs a binary signal indicating whether the voltage of the analog signal is higher or lower than the reference voltage; Prepared,
The diagnosis processing unit
An abnormality diagnosing apparatus configured to perform Fourier transform processing by extending a signal from the comparator to a predetermined data width.

Images