Diagnostic device, diagnostic system, power conversion device, and diagnostic method

Abstract

The diagnostic device (2) includes a diagnostic signal generating unit (23) that generates a diagnostic signal including at least one of information on a detected value of a motor current that is a current flowing through the motor (3), a motor current command value that is a command value for the motor current, a torque command value that is a command value for a motor torque to be applied to the motor (3), a motor torque expressed as a function of the motor current, an acceleration of the motor (3), a speed of the motor (3), and a disturbance torque applied to the motor (3) as a disturbance, a sine wave signal generating unit (24) that receives as input a motor position that is position information of the motor (3) and uses a value obtained by multiplying the motor position by a coefficient for diagnosis and generates a sine wave signal as a function of a frequency that varies in proportion to the coefficient in response to speed fluctuations of the motor (3), and a product-sum calculation unit (22) that calculates the product-sum of the diagnostic signal and the sine wave signal.

Description

[Technical field]

[0001] The present disclosure relates to a diagnostic device that diagnoses the state of a load device and peripheral devices connected to a motor, a diagnostic system and a power conversion device that include the diagnostic device, and a diagnostic method. [Background technology]

[0002] When a sudden failure occurs in a motor such as a servo motor, a load device driven by the motor, or peripheral equipment, the operation of the power conversion device that drives these devices must be stopped until repairs are completed. In addition to stopping the operation of the power conversion device, it is also assumed that abnormalities will occur in unintended places due to the impact of the failure, which may extend the failure period. Examples of load devices mentioned here are XY stages, linear guides, robot arms, etc. Examples of peripheral devices are couplings, reducers, bearings, etc.

[0003] Detecting signs of abnormalities before equipment breaks down, in other words, performing predictive maintenance, is important from the perspective of reducing downtime, improving productivity, and ensuring stable operation, not just for power conversion equipment.

[0004] Several conventional techniques related to predictive maintenance are known. The first conventional technique is a technique for performing frequency analysis on time-series data obtained by monitoring the state of a device, and identifying the cause of the abnormality while capturing signs of abnormality in the device based on information in the analyzed frequency domain. However, this technique is based on the premise that the motor is driven at a constant rotation speed and that the frequency indicating signs of abnormality does not increase or decrease. When the motor is a servo motor, the servo motor is usually operated by repeatedly accelerating and decelerating. For this reason, this technique, which is based on the premise that the motor is operated at a constant rotation speed, is not suitable for diagnosing a device that drives a servo motor.

[0005] The second method according to the prior art is to operate the device with a dedicated diagnostic operation pattern separately from normal operation, and determine the state of the device based on the data obtained at that time. However, this method has a problem that productivity decreases because normal operation is not possible when the device is operated with the dedicated diagnostic operation pattern. In addition, there is a problem that if the dedicated diagnostic operation pattern is significantly different from the operation pattern during operation, it is impossible or difficult to detect signs of abnormality during normal operation.

[0006] A third method according to the related art is a method of frequency analysis of time series data obtained by monitoring the state of a device while utilizing the rotational position and rotational speed of a motor. Patent Document 1 is known as a document disclosing this method. Patent Document 1 discloses a technology for acquiring information about rotation obtained from an input shaft or an output shaft of a rotating device, fitting the acquired information about rotation with a basis function having the rotational position of the input shaft or the output shaft of the rotating device as a variable, and extracting a frequency component related to the rotation of the input shaft or the output shaft of the rotating device based on the fitting result.

[0007] The technology of Patent Document 1 has the feature of being able to extract frequency components without being affected by the rotation speed of a rotating device. In the technology of Patent Document 1, the fitting calculation, which is a calculation process related to fitting to a basis function, is treated as a problem of parameter estimation using the least squares method.

[0008] The least squares method can be roughly divided into a lump-sum least squares method that processes input and output data in a lump to estimate parameters, and a sequential least squares method that processes input and output data sequentially to estimate parameters. Here, the lump-sum least squares method requires the calculation of an inverse matrix, but the calculation of an inverse matrix has a problem that the calculation load is large and that the inverse matrix itself may not exist. For this reason, it is considered that the lump-sum least squares method is not suitable for this type of fitting calculation. Although it is not possible to confirm whether this is taken into consideration, the sequential least squares method is used in Patent Document 1. In addition to the least squares method, batch gradient descent, stochastic gradient descent, and the like are known for fitting to basis functions.

[0009] A brief explanation of the recursive least squares method is given below. The recursive least squares method sequentially calculates the estimated value ψ^ of the parameter ψ at time t. The calculation formula for the estimated value ψ^ can be expressed by the following equations (1) and (2). Note that the parameter is often expressed as θ, but here we use ψ.

[0010]

number

number

[0011] In the above equations (1) and (2), ψ^(t) and ψ^(t-1) are the estimates of the parameter ψ at time t and time t-1, respectively. φ(t) is the basis function at time t. P(t) and P(t-1) are the covariance matrices at time t and time t-1, respectively. y(t) is the output value at time t, and λ is the forgetting factor. The forgetting factor λ is a real number greater than 0 and less than or equal to 1. [Prior art documents] [Patent documents]

[0012] [Patent Document 1] JP 2015-034776 A Summary of the Invention [Problem to be solved by the invention]

[0013] As mentioned above, in Patent Document 1, the recursive least squares method is used for the calculation process related to fitting to the basis function. However, the recursive least squares method has the problem that it takes a long time for the calculation to converge and the parameter estimation process may diverge if the initial value of the covariance matrix and the value of the forgetting factor are not appropriate. Similar problems cannot be avoided not only in the recursive least squares method but also in other fitting methods. For example, the batch gradient descent method has the problem that the calculation load is large, and the stochastic gradient descent method has the problem that it is easily affected by noise and the result may not converge. In addition, both methods have the problem that it takes a long time to converge and the parameter estimation process may diverge if the value of the learning rate, which means the weight for the gradient, is not set appropriately.

[0014] As described above, with the conventional techniques, it has been difficult to perform highly accurate diagnosis while reducing the calculation processing time.

[0015] The present disclosure has been made in consideration of the above, and has an object to provide a diagnostic device that can perform highly accurate diagnosis while shortening the calculation processing time. [Means for solving the problem]

[0016] In order to solve the above-mentioned problems and achieve the object, a diagnostic device according to the present disclosure is a diagnostic device for diagnosing the state of a motor, the state of a peripheral device connected to the motor, or the state of a load device connected to the motor, and includes a diagnostic signal generating unit, a sine wave signal generating unit, and a product-sum calculation unit. The diagnostic signal generating unit generates a diagnostic signal including at least one of information on a detected value of a motor current that is a current flowing through the motor, a motor current command value that is a command value of the motor current, a torque command value that is a command value of a motor torque to be applied to the motor, a motor torque expressed as a function of the motor current, an acceleration of the motor, a speed of the motor, and a disturbance torque applied to the motor as a disturbance. The sine wave signal generating unit receives a motor position that is position information of the motor, and uses a value obtained by multiplying the motor position by a coefficient for diagnosis to generate a sine wave signal having a function of a frequency that varies in proportion to the coefficient according to the speed fluctuation of the motor. The product-sum calculation unit calculates the product-sum of the diagnostic signal and the sine wave signal. Effect of the Invention

[0017] The diagnostic device according to the present disclosure has the advantage of being able to perform highly accurate diagnosis while shortening the calculation processing time. [Brief description of the drawings]

[0018] [Figure 1] FIG. 1 is a diagram showing a configuration example of an electrical device system including a diagnostic device according to a first embodiment; [Diagram 2] FIG. 1 is a diagram showing another example of the configuration of an electrical device system including a diagnostic device according to the first embodiment; [Diagram 3] FIG. 1 is a diagram showing a configuration example of a diagnostic device according to a first embodiment configured as a single device. [Figure 4] FIG. 1 shows an example of the configuration of a product-sum calculation unit and a sine wave signal generation unit according to a first embodiment; [Diagram 5] FIG. 13 is a diagram showing another example of the configuration of the product-sum calculation unit and the sine wave signal generation unit according to the first embodiment; [Figure 6] FIG. 1 is a diagram showing an example of a processing result by the diagnostic device according to the first embodiment; [Figure 7] FIG. 1 is a diagram for explaining timing of current detection in the first embodiment; [Figure 8] FIG. 10 is a diagram for explaining the timing of motor position detection in the first embodiment; [Figure 9] FIG. 1 is a block diagram showing an example of a hardware configuration for implementing the functions of a diagnostic device according to a first embodiment. [Figure 10] FIG. 11 is a block diagram showing another example of a hardware configuration for implementing the functions of the diagnostic device according to the first embodiment. [Figure 11] FIG. 13 is a diagram showing a configuration example of an electrical device system including a diagnostic device according to a second embodiment. [Figure 12] FIG. 13 is a diagram showing a configuration example of an electrical device system including a diagnostic device according to a third embodiment. [Figure 13] FIG. 13 is a diagram showing a configuration example of an electrical device system including a diagnostic device according to a fourth embodiment. [Figure 14] FIG. 13 is a diagram showing a first example of a window function in the fourth embodiment. [Figure 15] FIG. 23 is a diagram showing a second example of a window function in the fourth embodiment. [Figure 16] FIG. 13 is a diagram showing an example of a processing result by the diagnostic device according to the fourth embodiment. [Figure 17] FIG. 13 is a diagram showing a configuration example of an electrical device system including a diagnostic device according to a fifth embodiment. [Figure 18] FIG. 13 is a diagram showing an example of a filter characteristic of a filter section according to the fifth embodiment; [Figure 19] FIG. 13 is a diagram for explaining other key features of the diagnostic device according to the fifth embodiment. [Figure 20] FIG. 13 is a diagram showing an example of a filter characteristic suitable for a filter unit according to the fifth embodiment; [Figure 21] FIG. 13 is a diagram showing a configuration example of an electrical device system including a diagnostic device according to a sixth embodiment. [Figure 22] FIG. 23 is a diagram for explaining the effect of a filter unit according to the sixth embodiment; [Diagram 23] FIG. 23 is a diagram showing a first configuration example of an electric device system including a diagnostic device according to a seventh embodiment. [Figure 24] FIG. 23 is a diagram showing a second configuration example of an electric device system including a diagnostic device according to a seventh embodiment. [Diagram 25]FIG. 23 is a diagram showing a third configuration example of an electric device system including a diagnostic device according to a seventh embodiment. [Figure 26] FIG. 23 is a diagram showing a fourth configuration example of an electric device system including a diagnostic device according to a seventh embodiment. [Figure 27] FIG. 23 is a diagram for explaining the effects of a coefficient array change unit and a fast Fourier transform unit according to the seventh embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0019] A diagnostic device, a diagnostic system, a power conversion device, and a diagnostic method according to embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. Note that for ease of explanation, the subscripts may be omitted for symbols with subscripts.

[0020] Embodiment 1 FIG. 1 is a diagram showing a configuration example of an electric equipment system 100 including a diagnostic device 2 according to a first embodiment. In FIG. 1, the electric equipment system 100 is composed of a power conversion device 1 and a diagnostic device 2. FIG. 1 is a diagram showing a configuration example in which the diagnostic device 2 is provided outside the power conversion device 1 for supplying operating power to the motor 3. In FIG. 1, the motor 3 is illustrated as a three-phase motor connected to three-phase power lines of UVW, but is not limited to a three-phase motor. The motor 3 may be a single-phase motor or a multi-phase motor having four or more phases. In this paper, as an example, the motor 3 is described as a three-phase motor. In addition, the motor 3 may perform either a rotary motion or a linear motion. In the latter case, it is particularly called a linear motor. In this paper, the term "motor" is a concept that includes a "linear motor".

[0021] Further, the motor 3 is connected to a load device 7, which is an object to be rotated or moved linearly, via a peripheral device 6. Examples of the load device 7 include a compressor, a drill, a robot arm, and a ball screw. In other words, the load device 7 is a general term for devices that require torque or thrust. Examples of the peripheral device 6 include a coupling, a belt, a reducer, a chain, and a bearing. Note that, although the motor 3, the peripheral device 6, and the load device 7 are shown as separate entities in FIG. 1, this is not limiting. Some of these components may be configured as an integrated unit without being distinguished as separate entities.

[0022] The diagnostic device 2 according to the first embodiment has a function of diagnosing the state of the motor 3, the state of the peripheral device 6 connected to the motor 3, or the state of the load device 7 connected to the motor 3. To achieve this function, the diagnostic device 2 includes a determination unit 21, a product-sum calculation unit 22, a diagnostic signal generation unit 23, a sine wave signal generation unit 24, and a motor position calculation unit 50, as shown in FIG.

[0023] A motor current detector 4 is provided on the electric wiring 8 connecting the power conversion device 1 and the motor 3 to detect the motor current that flows through the motor 3. The motor current detector 4 detects the three-phase current i u ,i v ,i w is detected as the motor current. A typical example of the motor current detection unit 4 is a current sensor such as a CT (Current Transformer), but is not limited to this example. The motor current detection unit 4 may be a magnetic sensor such as a Hall element, or may be configured to detect the motor current using a shunt resistor.

[0024] In FIG. 1, the motor current detectors 4 are arranged in the U and V phases, but the motor current detector 4 is not arranged in the W phase. However, the W phase current i w i u +i v +i w= 0. In other words, the motor current detector 4 may be disposed in any two of the phases U, V, and W. The three-phase current i detected by the motor current detector 4 is u ,i v ,i w The detected value is input to the diagnostic signal generating section 23 of the diagnostic device 2.

[0025] A position sensor 5 is attached to the motor 3. The position sensor 5 detects the motor position, which is position information of the motor 3. The detected value of the motor position is input not only to the power conversion device 1 but also to a motor position calculation unit 50 of the diagnosis device 2. If the motor 3 rotates, the motor position corresponds to the rotation angle θ (rad) indicating the rotation position of the motor 3. If the motor 3 moves linearly, the motor position corresponds to the position x (m) of the motor 3, or a value (rad) obtained by multiplying the position x (m) by a conversion coefficient (rad / m). In this paper, since the motor 3 is assumed to be a three-phase motor that rotates, the explanation will be given assuming that the motor position is the rotation angle θ (rad).

[0026] Motor position calculation unit 50 calculates rotation angle θ based on the detected value of the motor position output from position sensor 5. The rotation angle θ calculated by motor position calculation unit 50 is input to diagnostic signal generation unit 23 and sine wave signal generation unit 24. Inside diagnostic signal generation unit 23, three-phase current i u ,i v ,i w is the current signal on the dq axis rotation coordinate system, d and q-axis current i q is converted to

[0027]

number

[0028] In the above formula (3), p represents the number of pole pairs. Three-phase current i u ,i v ,i wis an AC signal, while the d-axis current i d and q-axis current i q is a DC signal when viewed on the dq axis rotating coordinate system.

[0029] The motor position, which is the position information of the motor 3, does not have to be detected by the position sensor 5, but may be obtained by estimation calculation as described later. The estimated value of the rotation angle θ obtained by the estimation calculation is called the "estimated rotation angle" and is represented by θ^. In this case, the above formula (3) can be expressed as the following formula (4) using the estimated rotation angle θ^.

[0030]

number

[0031] The diagnostic signal generating unit 23 outputs the rotation angle θ or an estimated rotation angle θ^, which is an estimate of the rotation angle θ, and the d-axis current i d and q-axis current i q Based on this, a diagnostic signal τ is generated and output to the product-sum calculation unit 22.

[0032] In recent years, surface permanent magnet synchronous motors (SPMSM) that use magnet torque and interior permanent magnet synchronous motors (IPMSM) that use both magnet torque and reluctance torque have been attracting attention as highly efficient and high-output motors. The motor torque, which is the torque output by a motor, is expressed as the d-axis current i d and q-axis current i q In this paper, for the sake of simplicity, we consider an SPMSM that uses only magnet torque. In the case of an SPMSM, the motor torque is expressed as a function of the q-axis current i q It can be expressed as a function of

[0033] The diagnostic signal generating unit 23 receives the rotation angle θ or an estimated rotation angle θ^, which is an estimate of the rotation angle θ, and the q-axis current i qBased on this, a diagnostic signal τ is generated and output to the product-sum calculation unit 22. The diagnostic signal τ is q Also, the motor torque and the q-axis current i q Since there is a linear relationship between the q-axis current i q The coefficient γ expressed as motor torque / current t Alternatively, a torque command value or a motor current command value generated in the power conversion device 1 may be output as the diagnostic signal τ. In addition, a q-axis current i q changes the acceleration of the rotor or mover (not shown) of the motor 3. Integrating the acceleration once gives the speed, and further integrating the speed gives the position. Therefore, information on the motor acceleration or motor speed may be output as the diagnostic signal τ. Furthermore, the diagnostic signal τ does not need to be one piece of information, and may include multiple pieces of information.

[0034] As described above, by utilizing the motor current, it is no longer necessary to place a torque meter between the motor 3 and the load device 7 to directly detect the torque. A torque meter requires a display, power supply, and other peripheral devices 6, which increases costs and increases the size of the device. In addition, it is necessary to use multiple couplings before and after the torque meter, which may reduce rigidity and worsen response speed. These problems can be avoided if a torque meter is not used.

[0035] Moreover, the torque applied to the motor 3 as a disturbance is a torque that indicates a symptom of a failure in the motor 3, the peripheral device 6, or the load device 7. If this torque is called "disturbance torque" and represented by d, the disturbance torque d can be estimated and calculated using the following equation (5).

[0036]

number

[0037] In the above equation (5), J is the moment of inertia, ω is the motor speed, and dω / dt is the motor acceleration, which is the rotational angular acceleration of the motor 3. f(s) is a function representing the characteristics of a filter provided to reduce unnecessary noise such as quantization error. The diagnostic signal generating unit 23 may output the calculated value of the disturbance torque d expressed by the above equation (5) to the product-sum calculating unit 22 as the diagnostic signal τ.

[0038] From the above, the diagnostic signal generating unit 23 generates a diagnostic signal τ including information on at least one of the motor current, the motor current command value which is a command value for the motor current, the torque command value which is a command value for the motor torque to be applied to the motor 3, the motor torque expressed as a function of the motor current, the motor acceleration dω / dt which is the acceleration of the motor 3, the motor speed ω which is the speed of the motor 3, and the disturbance torque d which is applied to the motor 3 as a disturbance.

[0039] The sine wave signal generating unit 24 uses the rotation angle θ and the coefficient k to output two signals shown in the following equation (6).

[0040]

number

[0041] In the above formula (6), the coefficient k is a coefficient for diagnosis and takes any real value. The coefficient k may be a coefficient array consisting of multiple coefficients, as described later. In this paper, the two signals calculated according to the above formula (6) may be referred to as "sine wave signals."

[0042] A more specific description will now be given. First, the following Table 1 is a chart showing an example of the characteristics of a bearing. The bearing is an example of the peripheral device 6.

[0043] [Table 1]

[0044] If there is an abnormality such as a scratch on a bearing, an abnormal characteristic frequency component appears in the vibration or current corresponding to the part of the bearing. Table 1 shows the abnormal characteristic frequencies and their orders that appear in the outer ring, cage, and rolling elements, which are the parts of the bearing. z is the number of rolling elements, d is the diameter of one rolling element, D is the pitch diameter of the rolling elements, β is the contact angle, and f r is the rotation frequency. Rotation frequency f r is the rotation speed of the motor 3, and the motor speed ω is r =ω / 2π. As shown in Table 1, the anomalous feature frequency is proportional to the rotation frequency f r The order is the function of the abnormal feature frequency f r The order is the rotation frequency f r It is possible to obtain this in advance as a constant value that does not depend on the load. By adopting this order for k in the above-mentioned equation (6), it becomes possible to detect bearing abnormalities by identifying their locations.

[0045] From the above, the sine wave signal generating unit 24 receives the rotation angle θ, which is the motor position, as an input, and uses a value obtained by multiplying the rotation angle θ by a coefficient k for diagnosis to generate a sine wave signal having a frequency that varies in proportion to the coefficient k in response to speed fluctuations of the motor 3. The sine wave signal generated by the sine wave signal generating unit 24 is input to the product-sum calculation unit 22 together with the diagnostic signal τ generated by the diagnostic signal generating unit 23.

[0046] The product-sum calculation unit 22 calculates the product-sum of the diagnostic signal τ and the sine wave signal. Specifically, the product-sum calculation unit 22 calculates the magnitude m of the feature at the coefficient k based on the following equation (7): k is calculated by multiply-and-accumulate.

[0047]

number

[0048] In the above formula (7), N is the number of data points, and the subscript i means the signal at time i. The feature size m kAs shown in the third formula, it can be calculated by the square root of the sum of the square of the value a calculated by the first formula and the square of the value b calculated by the second formula. k It is not necessary to use the third formula. For example, the square root calculation can be omitted and m k =a k 2 +b k 2 The formula for dividing by the number of data points N is the feature size m k This is to make the value independent of the number of data points N, but this calculation can also be omitted.

[0049] In addition, the summation of the first and second equations in the above equation (7) can be regarded as a pseudo-integral operation, and the value a at time n-1 can be expressed as in the following equation (8): k (n-1) - value a at time n k A sequential calculation formula using a low pass filter (LPF) may be used to obtain (n).

[0050]

number

[0051] In the above formula (8), ω c is the cutoff frequency of the pseudo-integral, and T s is the sampling period.

[0052] The feature size m calculated by the product-sum calculation unit 22 k is input to the determination unit 21. The determination unit 21 determines the feature size m k For example, the size of the feature m k Then, the size of the input feature m k The size of the features learned in normal times is m k If the trend deviates from the normal trend, it can be determined that there is an abnormality. k can be used as diagnostic data to identify the site of an abnormality.

[0053] In FIG. 1, the diagnostic device 2 is shown as a device provided outside the power conversion device 1, but is not limited to this configuration. The diagnostic device 2 may be provided inside the power conversion device 1 as shown in FIG. 2. FIG. 2 is a diagram showing another configuration example of an electric equipment system 100 including the diagnostic device 2 according to the first embodiment. In FIG. 2, the power conversion device 1 is configured to have a diagnostic function by the diagnostic device 2. When the power conversion device 1 is configured to have a diagnostic function, there is an advantage that the detection value used by the power conversion device 1 can be directly used for the diagnostic function, making it easier to handle signals. In addition, when the power conversion device 1 is configured to have a diagnostic function, there is also an advantage that there is no need to install a sensor dedicated to the diagnostic function.

[0054] 2, the power conversion device 1 includes the diagnostic device 2 described in FIG. 1. The power conversion device 1 also includes a power conversion unit 10 that controls the driving of a motor 3. The power conversion unit 10 includes a voltage command value calculation unit 11, an inverter 12, and a UVW / dq conversion unit 13, and further includes the motor current detection unit 4 and a motor position calculation unit 50 described in FIG. 1. The UVW / dq conversion unit 13 converts the three-phase current i u ,i v ,i w The d-axis current i on the dq-axis rotating coordinate system d and q-axis current i q This calculation requires information on the rotation angle θ, so in FIG. 2, motor position calculation unit 50 is shown as a component of power conversion unit 10. However, motor position calculation unit 50 does not need to be a component of power conversion unit 10, and may be a component of diagnostic device 2 as in FIG. 1.

[0055] The voltage command value calculation unit 11 calculates the d-axis current i d , q-axis current i qand the rotation angle θ, a voltage command value for controlling the operation of the motor 3 is calculated and output to the inverter 12. Inside the inverter 12, a pulse width modulation (PWM) signal is generated using the voltage command value output from the voltage command value calculation unit 11 and a carrier signal which is a function of the carrier frequency. In the inverter 12, a switching element (not shown) operates in accordance with the PWM signal, and a desired voltage is generated and applied to the motor 3.

[0056] 2, the diagnostic device 2 can utilize information detected by the power conversion unit 10 and information calculated by the power conversion unit 10. The configuration of FIG. 2 has the advantage that it is not necessary to install new sensors in the diagnostic device 2 because the sensors inherently provided in the power conversion unit 10 can be utilized.

[0057] In addition, in FIG. 2, the diagnostic signal generating unit 23 receives the rotation angle θ and the q-axis current i q In addition, both of the rotation angle θ and the q-axis current i q In addition to both, the d-axis current i d The diagnostic signal generating unit 23 may be included in the power conversion unit 10.

[0058] 2 is an example of the power conversion device 1 having a diagnostic function, and the functional division between the power conversion unit 10 and the diagnostic device 2 is not limited to that shown in FIG. 2. For example, the q-axis current i q When directly converting this to the diagnostic signal τ, there is no need to generate or calculate a new signal in the diagnostic signal generating unit 23. In this case, the diagnostic signal generating unit 23 is not necessary, and the function of the diagnostic signal generating unit 23 can be substituted by the UVW / dq converting unit 13. In addition, in FIG. 2, the motor position information is acquired from the position sensor 5, but as will be described in the third embodiment later, an estimated rotation angle θ^ that is an estimated value of the rotation angle θ may be used instead of the rotation angle θ.

[0059] In addition, in Fig. 2, the diagnostic device 2 is shown as a device provided inside the power conversion device 1, but is not limited to this configuration. Fig. 3 is a diagram showing a configuration example in which the diagnostic device according to the first embodiment is configured as a standalone device. As shown in Fig. 3, the diagnostic device 2 may be configured as a device that operates independently. Also, the diagnostic device 2 may be configured to be attached to a device other than the power conversion device and operate.

[0060] Fig. 4 is a diagram showing an example of the configuration of the product-sum calculation unit 22 and the sine wave signal generation unit 24 according to the first embodiment. Fig. 4 shows an example of the configuration in which a coefficient k is not one but a plurality of coefficients k are used. The plurality of coefficients k are k1, k2, ..., and the corresponding feature magnitudes m k are respectively designated as m1, m2, .... Values ​​a1 and b1 are calculated based on coefficient k1, and feature size m1 is calculated based on values ​​a1 and b1. Values ​​a2 and b2 are calculated based on coefficient k2, and feature size m2 is calculated based on values ​​a2 and b2.

[0061] Fig. 5 is a diagram showing another example of the configuration of the product-sum calculation unit 22 and the sine wave signal generation unit 24 according to embodiment 1. As shown in Fig. 5, values ​​a1, b1 and values ​​a2, b2 for calculating feature magnitudes m1, m2 may be calculated using an LPF.

[0062] Fig. 6 is a diagram showing an example of a processing result by the diagnostic device 2 according to the first embodiment. At the top of Fig. 6, the motor speed (rpm) is shown, and at the top center of Fig. 6, the waveform of the sine wave signal generated by the sine wave signal generating unit 24 is shown. At the bottom center of Fig. 6, the waveform of the diagnostic signal generated by the diagnostic signal generating unit 23 is shown in a dashed line when normal, and in a solid line when abnormal. At the bottom of Fig. 6, the waveform of the product-sum operation output output by the product-sum operation unit 22 is shown in a dashed line when normal, and in a solid line when abnormal. The horizontal axis represents time in seconds (s).

[0063] Here, an example will be described in which the condition of a servo motor and bearings are monitored and diagnosed. A servo motor accelerates and decelerates in a short time of less than one second. To accurately calculate the degree of abnormality from data of this short time, fast convergence to the true value is required. Also, the detected value of the motor current may include the influence of friction torque caused by the bearings inside the servo motor depending on the ambient temperature or the duration of operation. As a result, the detected value of the motor current contains noise components that can cause errors, ranging from components close to DC to high frequency components on the order of kHz. For this reason, it is necessary to remove these noise components in the processing of the diagnosis device 2.

[0064] The calculation process by the diagnostic device 2 is resistant to noise, and if the speed of convergence to the true value is not sufficient, the monitoring results of the servo motor, which operates in a short time, will vary. If the variation is large, it becomes difficult to appropriately set the threshold value that distinguishes between normal and abnormal, and there is a risk of misjudging a normal state as abnormal.

[0065] On the other hand, when monitoring the condition using the product-sum operation output, as can be seen from the product-sum operation output waveform at the bottom of Figure 6, normal waveforms and abnormal waveforms are separated, making it possible to appropriately set a threshold value for distinguishing between normal and abnormal.

[0066] Furthermore, harmonic components are superimposed on the motor current due to the switching operation of the inverter 12. These harmonic components are superimposed on signal components indicating abnormal states of the load devices including the motor 3, the peripheral devices 6, and the load devices 7, and therefore become a factor in increasing noise, leading to variations in the monitoring results. In the first embodiment, in order to suppress variations in the monitoring results, processing is performed with the following points in mind.

[0067] Fig. 7 is a diagram for explaining the timing of current detection in the first embodiment. Fig. 7 shows the operation of sampling the current at the timing of the peaks and valleys of a carrier signal that is a triangular wave. Note that a triangular wave is an example of a carrier signal, and a waveform other than a triangular wave may be used as long as the signal has a waveform in which the amplitude of the signal waveform varies periodically with time.

[0068] When the motor 3 is a three-phase motor, the voltage applied to the motor 3 has three phases, UV and W, but for ease of explanation, only the U phase is shown in FIG. 7. At the top of FIG. 7, the waveform of the carrier signal is shown by a solid line, and the modulated wave of the U phase is shown by a dashed line. At the top center of FIG. 7, the waveform of the U phase voltage applied to the motor 3 is shown by a solid line, and at the bottom center of FIG. 7, the waveform of the U phase current flowing through the motor 3 is shown by a solid line. At the bottom of FIG. 7, the waveform of the q-axis current converted by the UVW / dq conversion unit 13 is shown by a dashed line. Note that the time on the horizontal axis and the signal magnitude on the vertical axis are normalized, and both are in units of (pu).

[0069] In FIG. 2, the circuit configuration of the inverter 12 is omitted. However, as is well known, in a three-phase inverter 12, one phase leg in which an upper arm switching element and a lower arm switching element are connected in series is connected in a three-phase bridge configuration.

[0070] The operation of FIG. 7 will be described below. First, the modulated wave of the U phase is compared with the carrier signal, and in the section where the modulated wave is larger than the carrier signal, the upper switching element is turned on and a positive voltage is applied to the U phase. On the other hand, in the section where the modulated wave is smaller than the carrier signal, the lower switching element is turned on and a negative voltage is applied to the U phase. The U phase current and the q-axis current have pulsating components near the carrier frequency, which is the frequency of the carrier signal, and near multiples of the carrier frequency. On the other hand, if the motor current is detected at the timing of the peaks or valleys of the carrier signal as shown in FIG. 7, an average value with reduced influence of the pulsating components can be detected. The detection of the motor current may be performed at both the peaks and valleys, or may be performed at the timing of only the valleys, or may be performed at the timing of only the peaks. Alternatively, the average value of the values ​​detected at the timing of the peaks and valleys may be used as the detection value of the motor current.

[0071] Fig. 8 is a diagram for explaining the timing of motor position detection in embodiment 1. In the upper part of Fig. 8, the waveform of the carrier signal is shown by a solid line, in the center part of Fig. 8, the waveform of the q-axis current converted by UVW / dq conversion unit 13 is shown by a dashed line, and in the lower part of Fig. 8, the rotation angle indicating the motor position is shown by a two-dot chain line. The notations of the horizontal and vertical axes are the same as in Fig. 7.

[0072] FIG. 8 shows the operation of detecting the motor position at the timing of the peaks and valleys of the carrier signal. The q-axis current, which is proportional to the torque, changes the acceleration of the rotor or the mover. The acceleration is integrated twice to obtain the position. If the pulsating component of the q-axis current is a sine wave, the pulsating component contained in the motor position obtained by integrating this sine wave twice becomes a negative sine wave obtained by inverting the sine wave. Therefore, the pulsating component of the current is suppressed at the timing of the peaks and valleys, and the pulsating component of the motor position is also suppressed. In addition, the smaller the moment of inertia and mass are, and the lower the carrier frequency is, the more problematic the pulsation of the motor position is. However, if the motor position is detected at the timing of the peaks and valleys of the carrier signal, these effects can be suppressed.

[0073] As described above, the diagnostic device according to the first embodiment is a diagnostic device for diagnosing the state of a motor, the state of a peripheral device connected to the motor, or the state of a load device connected to the motor, and includes a diagnostic signal generating unit, a sine wave signal generating unit, and a product-sum calculation unit. The diagnostic signal generating unit generates a diagnostic signal including at least one of information on a detection value of a motor current that is a current flowing through the motor, a motor current command value that is a command value of the motor current, a torque command value that is a command value of a motor torque to be applied to the motor, a motor torque expressed as a function of the motor current, the acceleration of the motor, the speed of the motor, and a disturbance torque applied to the motor as a disturbance. The sine wave signal generating unit receives the motor position that is position information of the motor as an input, and uses a value obtained by multiplying the motor position by a coefficient for diagnosis to generate a sine wave signal having a function of a frequency that varies in proportion to the coefficient according to the speed fluctuation of the motor. The product-sum calculation unit calculates the product-sum of the diagnostic signal and the sine wave signal. When the diagnostic signal is used, it is difficult to diagnose abnormality and normality with a threshold value with high accuracy. On the other hand, when the product-sum operation output obtained by multiplying and accumulating the diagnostic signal and the sine wave signal is used, the threshold value can be easily set, and the diagnostic accuracy can be improved compared to the case where only the diagnostic signal is used. Also, the product-sum operation output representing the magnitude of the feature can be obtained by sequential calculation without performing an inverse matrix calculation, and therefore the calculation processing time can be shortened. Therefore, according to the diagnostic device of the first embodiment, it is possible to obtain an effect that it is possible to perform a highly accurate diagnosis while shortening the calculation processing time.

[0074] The diagnostic method according to the first embodiment is a diagnostic method for diagnosing the state of a motor, the state of a peripheral device connected to the motor, or the state of a load device connected to the motor, and can be a process including the first to fourth steps shown below. In the first step, a motor current that is a current flowing through the motor is detected. In the second step, a diagnostic signal is generated that includes at least one piece of information among the motor current, the motor current command value that is a command value of the motor current, the torque command value that is a command value of the motor torque to be applied to the motor, the motor torque expressed as a function of the motor current, the motor acceleration, the motor speed, and the disturbance torque applied to the motor as a disturbance. In the third step, a sine wave signal is generated that is a function of a frequency that varies in proportion to the coefficient according to the speed fluctuation of the motor, using a value obtained by multiplying the motor position that is the position information of the motor by a coefficient for diagnosis and the motor position as an input. In the fourth step, the sum of products of the diagnostic signal and the sine wave signal is calculated. According to the diagnostic method according to the first embodiment, as with the diagnostic device according to the first embodiment, an effect is obtained in that it is possible to perform a highly accurate diagnosis while shortening the calculation processing time.

[0075] Furthermore, in the diagnostic device and diagnostic method according to the first embodiment, when the motor current is controlled by a pulse-width modulated signal generated using a carrier signal that is a function of the carrier frequency, it is desirable that the motor current input to the diagnostic signal generating unit is a value detected at the timing of the crest or valley of the carrier signal. Similarly, it is desirable that the motor position is a value detected at the timing of the crest or valley of the carrier signal. If the motor current and motor position are detected at the timing of the crest or valley of the carrier signal, it is possible to suppress pulsating components that may be included in the motor current and motor position, and therefore it is possible to obtain the effect of further improving the diagnostic accuracy.

[0076] The motor to be diagnosed may be not only a motor that receives electrical energy and outputs mechanical energy, but also a generator that receives mechanical energy and outputs electrical energy. The motor to be diagnosed may be not only an AC motor driven by AC power, but also a DC motor driven by DC power. These generators and DC motors can also be diagnosed using the above-mentioned method. It is not essential to have a judgment unit, and the user may obtain the result of the product-sum calculation and judge whether or not there is an abnormality.

[0077] At the end of the first embodiment, there will be described a hardware configuration for realizing the above-mentioned functions of the diagnostic device 2. Fig. 9 is a block diagram showing an example of a hardware configuration for realizing the functions of the diagnostic device 2 according to the first embodiment.

[0078] In order to realize the functions of the diagnostic device 2 according to the first embodiment, the configuration can include a processor 701 that performs calculations, a memory unit 702 that stores programs read by the processor 701, and an interface 704 that inputs and outputs signals, as shown in FIG. 9.

[0079] The processor 701 is an example of a calculation means. The processor 701 may be a calculation means called a microprocessor, a microcomputer, a microcontroller, a CPU (Central Processing Unit), or a DSP (Digital Signal Processor). Examples of the storage unit 702 include a non-volatile or volatile semiconductor memory such as a RAM (Random Access Memory), a ROM (Read Only Memory), a flash memory, an EPROM (Erasable Programmable ROM), or an EEPROM (registered trademark) (Electrically EPROM), a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, and a DVD (Digital Versatile Disc).

[0080] The storage unit 702 stores a program for executing the functions of the diagnostic device 2 according to the embodiment 1. The processor 701 receives and transmits necessary information via the interface 704, executes the program stored in the storage unit 702, and refers to the table stored in the storage unit 702, thereby performing the above-mentioned processing. The calculation results by the processor 701 can be stored in the storage unit 702.

[0081] Moreover, when the function of the diagnostic device 2 according to the first embodiment is realized, the configuration shown in FIG. 10 may be used. FIG. 10 is a block diagram showing another example of a hardware configuration for realizing the function of the diagnostic device 2 according to the first embodiment. In FIG. 10, the processor 701 shown in FIG. 9 is replaced with a processing circuit 703. The processing circuit 703 corresponds to a single circuit, a composite circuit, an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a combination of these. Information input to the processing circuit 703 and information output from the processing circuit 703 can be exchanged via an interface 704. Moreover, the processing result by the processing circuit 703 can be stored in the storage unit 702.

[0082] It should be noted that some of the processes in diagnostic device 2 may be performed by processing circuit 703 , and other processes not performed by processing circuit 703 may be performed by processor 701 .

[0083] Embodiment 2 Fig. 11 is a diagram showing a configuration example of an electric equipment system 100A including a diagnostic device 2 according to embodiment 2. Compared with the electric equipment system 100 shown in Fig. 1, in Fig. 11, a vibration noise observation unit 9 that observes vibration or noise of the motor 3 is provided in the motor 3. Also, in Fig. 11, the motor current detection unit 4 provided in the electric wiring 8 is omitted from the illustration. Note that the omission of the motor current detection unit 4 means that the detection value of the motor current detection unit 4 is not used in the processing of the diagnostic device 2 according to embodiment 2, and does not mean that the motor current detection unit 4 is unnecessary for the operation of the power conversion device 1.

[0084] An example of the vibration and noise observation unit 9 is a vibration meter or a sound level meter for observing the vibration or noise of the motor 3. The vibration and noise observation unit 9 observes the vibration or noise at one or more observation points. The observed values ​​observed by the vibration and noise observation unit 9 are input to the diagnostic signal generation unit 23. The diagnostic signal generation unit 23 generates a diagnostic signal τ based on the vibration and noise signal observed by the vibration and noise observation unit 9. The roles of the motor position calculation unit 50, the diagnostic signal generation unit 23, the sine wave signal generation unit 24, the product-sum calculation unit 22 and the judgment unit 21 are the same as those in the first embodiment described above. By using the diagnostic device 2 according to the second embodiment, the state of the device can be determined based on the observed values ​​of the vibration or noise, not the current or torque.

[0085] 11, the diagnostic device 2 is shown as a device provided outside the power conversion device 1, but is not limited to this configuration. As in the first embodiment, the diagnostic device 2 may be provided inside the power conversion device 1. When the power conversion device 1 is configured to have a diagnostic function, there is an advantage that the detection values ​​and the like used by the power conversion device 1 can be directly used for the diagnostic function, making it easier to handle signals. In addition, when the power conversion device 1 is configured to have a diagnostic function, there is also an advantage that there is no need to install a sensor dedicated to the diagnostic function.

[0086] As described above, the diagnostic device according to the second embodiment is a diagnostic device for diagnosing the state of a motor, the state of a peripheral device connected to the motor, or the state of a load device connected to the motor, and includes a diagnostic signal generating unit, a sine wave signal generating unit, and a product-sum calculating unit. The diagnostic signal generating unit generates a diagnostic signal including information on an observed value of noise or vibration of the motor. The sine wave signal generating unit receives the motor position, which is position information of the motor, as an input, and uses a value obtained by multiplying the motor position by a coefficient for diagnosis to generate a sine wave signal having a frequency as a function that varies in proportion to the coefficient according to speed fluctuation of the motor. The product-sum calculating unit calculates the product-sum of the diagnostic signal and the sine wave signal. The diagnostic device according to the second embodiment has the effect of being able to perform a highly accurate diagnosis while shortening the calculation processing time, similar to the diagnostic device according to the first embodiment.

[0087] The diagnostic method according to the second embodiment is a diagnostic method for diagnosing the state of a motor, the state of a peripheral device connected to the motor, or the state of a load device connected to the motor, and can be a process including the first to fourth steps shown below. In the first step, noise or vibration of the motor is observed. In the second step, a diagnostic signal including information on the observed value of the noise or vibration of the motor is generated. In the third step, a sine wave signal is generated using a value obtained by multiplying the motor position, which is position information of the motor, by a coefficient for diagnosis and the motor position, as an input. In the fourth step, a product sum of the diagnostic signal and the sine wave signal is calculated. According to the diagnostic method according to the second embodiment, as with the diagnostic device according to the second embodiment, it is possible to obtain an effect that it is possible to perform a highly accurate diagnosis while shortening the calculation processing time.

[0088] Embodiment 3 FIG. 12 is a diagram showing a configuration example of an electric equipment system 100B including a diagnostic device 2 according to a third embodiment. Compared with the electric equipment system 100 shown in FIG. 2, the position sensor 5 provided on the motor 3 is deleted in FIG. 12. In addition, the rotation angle θ indicating the motor position is input from the motor position calculation unit 50 to the diagnostic signal generation unit 23 and the sine wave signal generation unit 24 in FIG. 2, but the estimated rotation angle θ^, which is an estimated value of the rotation angle θ, is input from the motor position calculation unit 50 to the diagnostic signal generation unit 23 and the sine wave signal generation unit 24 in FIG. 12. The roles of the diagnostic signal generation unit 23, the sine wave signal generation unit 24, the product-sum calculation unit 22, and the judgment unit 21 are the same as those in the first and second embodiments described above. If the estimated rotation angle θ^ is used, the position sensor 5 can be eliminated, and wiring between the position sensor 5 and the diagnostic device 2 is also eliminated, thereby reducing the manufacturing cost of the device. In addition, since there is no need to consider failure of the position sensor 5, the reliability of the device can be improved.

[0089] In FIG. 12, the diagnostic device 2 is configured to be provided inside the power conversion device 1, but as in FIGS. 1 and 12, the diagnostic device 2 may be configured to be provided outside the power conversion device 1.

[0090] As described above, the diagnostic device according to the third embodiment is a diagnostic device for diagnosing the state of a motor, the state of a peripheral device connected to the motor, or the state of a load device connected to the motor, and includes a diagnostic signal generating unit, a sine wave signal generating unit, and a product-sum calculation unit. The diagnostic signal generating unit receives an estimated value of the motor position, which is position information of the motor, as an input, and generates a diagnostic signal including at least one of information on a detected value of a motor current, which is a current flowing through the motor, a motor current command value, which is a command value of the motor current, a torque command value, which is a command value of the motor torque applied to the motor, a motor torque expressed as a function of the motor current, the acceleration of the motor, the speed of the motor, and a disturbance torque applied to the motor as a disturbance. The sine wave signal generating unit receives an estimated value of the motor position as an input, and uses a value obtained by multiplying the motor position by a coefficient for diagnosis to generate a sine wave signal having a function of a frequency that varies in proportion to the coefficient according to the speed fluctuation of the motor. The diagnostic device according to the third embodiment has an effect of being able to perform a highly accurate diagnosis while shortening the calculation processing time, as in the diagnostic devices according to the first and second embodiments.

[0091] Moreover, the diagnostic method according to the third embodiment is a diagnostic method for diagnosing the state of a motor, the state of a peripheral device connected to the motor, or the state of a load device connected to the motor, and can be a process including the first to fourth steps shown below. In the first step, a motor current that is a current flowing through the motor is detected. In the second step, an estimated value of the motor position that is position information of the motor is input, and a diagnostic signal is generated that includes at least one piece of information among a detected value of the motor current, a motor current command value that is a command value of the motor current, a torque command value that is a command value of the motor torque to be applied to the motor, a motor torque expressed as a function of the motor current, the acceleration of the motor, the speed of the motor, and a disturbance torque applied to the motor as a disturbance. In the third step, an estimated value of the motor position is input, and a sine wave signal is generated using a value obtained by multiplying the estimated value of the motor position by a coefficient for diagnosis to generate a sine wave signal that is a function of a frequency that varies in proportion to the coefficient according to the speed fluctuation of the motor. In the fourth step, a product sum of the diagnostic signal and the sine wave signal is calculated. The diagnostic method according to the third embodiment, like the diagnostic methods according to the first and second embodiments, has the effect of enabling highly accurate diagnosis while shortening the calculation processing time.

[0092] Embodiment 4 Fig. 13 is a diagram showing a configuration example of an electric equipment system 100C including a diagnostic device 2 according to embodiment 4. Compared with the electric equipment system 100 shown in Fig. 2, in Fig. 13, a window function multiplication unit 25 is inserted between the diagnostic signal generation unit 23 and the product-sum calculation unit 22. The roles of the diagnostic signal generation unit 23, the sine wave signal generation unit 24, the product-sum calculation unit 22 and the determination unit 21 are the same as those of the above-mentioned embodiments 1 to 3.

[0093] Window function multiplication unit 25 receives diagnostic signal τ generated by diagnostic signal generation unit 23 and rotation angle θ output from motor position calculation unit 50. Window function multiplication unit 25 corrects diagnostic signal τ using the rotation angle θ, and outputs the corrected diagnostic signal τ' to product-sum calculation unit 22.

[0094] 14 is a diagram showing a first example of a window function in the fourth embodiment. The horizontal axis indicates the motor speed, and the vertical axis indicates the magnitude of the correction coefficient by which the diagnostic signal τ is multiplied. N1, N2, N3, and N4 are preset setting values. Examples of the setting values ​​will be described later.

[0095] In the first example shown in Fig. 14, when the motor speed is close to 0 or is low, the correction coefficient is set to a small value close to 0. This is to avoid the phenomenon in which the signal is saturated by the diagnostic signal τ close to a constant value, as will be described later.

[0096] Fig. 15 is a diagram showing a second example of a window function in the fourth embodiment. The units of the horizontal and vertical axes are the same as those in Fig. 14. N5 is a positive speed having a larger absolute value than N2, and N6 is a negative speed having a larger absolute value than N4.

[0097] In the second example shown in Fig. 15, the window function of the first example shown in Fig. 14 is further set so that the correction coefficient on the high speed side is also smaller. This is to prevent an overshoot from occurring in the motor current or motor speed when the motor speed reaches the target speed, causing a pulsation different from the pulsation that indicates an abnormality, and this pulsation from being input to the product-sum calculation unit 22 and affecting the calculation process of the product-sum calculation unit 22.

[0098] 14 and 15 show window functions that are functions of the motor speed, but the window function is not limited to these examples and may be a function of the motor acceleration.

[0099] FIG. 16 is a diagram showing an example of the processing result by the diagnostic device 2 according to the fourth embodiment. FIG. 6 shows an example of the operational waveform when a window function is not used, whereas FIG. 16 shows an example of the operational waveform when the window function shown in FIG. 14 is used. The notations on the horizontal and vertical axes are the same as those in FIG. 6. However, the waveform shown in the lower center of FIG. 6 is the diagnostic signal τ, whereas the waveform shown in the lower center of FIG. 16 is the corrected diagnostic signal τ'. The operational waveforms in FIG. 6 and FIG. 16 are both the results of calculation using the above-mentioned formula (8), that is, a calculation formula using an LPF. The calculation result in FIG. 16 is obtained by using the window function shown in FIG. 14, with N1=100 (rpm) 、 The settings are N2 = 300 (rpm), N3 = -100 (rpm), and N4 = -300 (rpm).

[0100] Due to the effect of the window function, the corrected diagnostic signal τ' has a value of 0 until time 0.25 (s). Also, the value of the product-sum operation output is 0 on average when normal, and has a positive value only when abnormal. In this way, by using the window function, saturation phenomenon can be easily avoided, enabling accurate diagnosis.

[0101] 13, a window function is applied to the output of the diagnostic signal generating unit 23, but a window function may be applied to the output of the sine wave signal generating unit 24. Even if a window function is applied to the output of the sine wave signal generating unit 24, a similar effect can be obtained.

[0102] Also, while Fig. 13 shows a configuration in which a window function is applied to the diagnostic device 2 of the electric equipment system 100 according to the first embodiment shown in Fig. 2, it is also possible to apply a window function to the diagnostic device 2 of the electric equipment system 100A according to the second embodiment shown in Fig. 11 and the diagnostic device 2 of the electric equipment system 100B according to the third embodiment shown in Fig. 12. Even with this configuration, it is possible to enjoy the effects of the fourth embodiment in addition to the effects of the respective embodiments.

[0103] As described above, the diagnostic device according to the fourth embodiment is the diagnostic device according to the first to third embodiments, further comprising a window function multiplying unit that multiplies the diagnostic signal by a window function having the motor speed or the motor acceleration as a function, corrects the diagnostic signal, and outputs the corrected diagnostic signal. The diagnostic device according to the fourth embodiment has an advantage that the phenomenon of the diagnostic signal being saturated can be easily avoided, and a highly accurate diagnosis can be made. The window function multiplying unit may be configured to multiply the sine wave signal by a window function having the motor speed or the motor acceleration as a function, corrects the sine wave signal, and outputs the corrected sine wave signal.

[0104] Embodiment 5. Fig. 17 is a diagram showing a configuration example of an electric equipment system 100D including a diagnostic device 2 according to embodiment 5. Compared with the electric equipment system 100 shown in Fig. 2, in Fig. 17, a filter unit 26 is inserted between the diagnostic signal generating unit 23 and the product-sum calculating unit 22. The roles of the diagnostic signal generating unit 23, the sine wave signal generating unit 24, the product-sum calculating unit 22 and the determining unit 21 are the same as those of the above-mentioned embodiments 1 to 4.

[0105] The diagnostic signal τ generated by the diagnostic signal generating unit 23 is input to the filter unit 26. The filter unit 26 corrects the diagnostic signal τ using its own filter characteristics, and outputs the corrected diagnostic signal τ′ to the product-sum calculation unit 22.

[0106] Fig. 18 is a diagram showing an example of the filter characteristic of the filter unit 26 according to the fifth embodiment. The horizontal axis of Fig. 18 is frequency, and the vertical axis is gain. Fig. 18 shows the characteristic of a high-pass filter having a cutoff frequency that attenuates the low-frequency side of the frequency. If a high-pass filter with such characteristics is used, the low-frequency components of the diagnostic signal output by the diagnostic signal generating unit 23 are reduced and output, so that it is possible to obtain the same effect as the diagnostic device 2 according to the fourth embodiment using a window function.

[0107] FIG. 19 is a diagram for explaining another key feature of the diagnostic device 2 according to the fifth embodiment. In the load device including the motor 3, the peripheral device 6, and the load device 7, there is a concept of a resonance frequency that is determined by, for example, the rigidity of the coupling, the belt, etc., and the moment of inertia around the rotation shaft of the motor 3. This resonance frequency is the frequency at which a resonance phenomenon occurs in the load device, and is basically a frequency that is independent of the motor speed, which is the rotation speed of the motor 3. Also, in the above Table 1, for bearings, the abnormality characteristic frequency and the rotation frequency f corresponding to the motor speed are listed. r He explained the relationship.

[0108] Figure 19 shows the relationship between the kth frequency, which changes according to the motor speed when the resonance phenomenon occurs, and the resonance frequency, which is independent of the motor speed. The horizontal axis of Figure 19 is the motor speed, and the vertical axis is the frequency. In this paper, the rotation frequency f r The frequency obtained by multiplying by a coefficient k is defined as the "kth frequency". In particular, when k is 1, it is called the "first frequency". Since the kth frequency is proportional to the rotation speed, it is a straight line that slopes upward to the right, as shown in Figure 19. In Figure 19, the line of the first frequency where k=1 is shown as a thick dashed line, and two lines of kth frequency with two different k>1, and two lines of kth frequency with two different k<1 are shown as dashed lines.

[0109] Since the line of the kth order frequency rises to the right, at a certain motor speed, the kth order frequency and the resonance frequency coincide. In FIG. 19, the motor speeds at which the kth order frequency and the resonance frequency coincide are ω1, ω2, ω3, and ω4, and the points at which they coincide are indicated by white circles. At these motor speeds ω1, ω2, ω3, and ω4, for example, the characteristics of the bearings shown in Table 1 are mixed with the characteristics of the resonance phenomenon. The resonance phenomenon increases the level of the diagnostic signal, but this does not necessarily mean that the abnormality characteristics have increased. Furthermore, the increase in the level of the diagnostic signal due to the resonance phenomenon leads to a decrease in the accuracy of the diagnosis.

[0110] FIG. 20 is a diagram showing an example of filter characteristics suitable for the filter unit 26 according to the fifth embodiment. FIG. 20 shows filter characteristics that attenuate components of a specific center frequency and its neighboring frequencies. As shown in FIG. 20, the gain is reduced at the center frequency and its neighboring frequency components, and the gain is not reduced at other frequencies. By setting this center frequency equal to the resonance frequency, it is possible to solve the above problem in which the characteristics of an abnormality in the load device and the characteristics of a resonance phenomenon that may occur in the load device are mixed together. Such a filter characteristic G(s) can be expressed by the following formula (9).

[0111]

number

[0112] The above equation (9) represents the characteristics of a filter called a notch filter, where c is a coefficient that determines the depth of the notch, Q is a coefficient that determines the width of the notch, and ω n is the center frequency.

[0113] As described above, the diagnostic device according to the fifth embodiment is the diagnostic device according to the first to third embodiments, further comprising a filter unit that reduces low-frequency components of the diagnostic signal output by the diagnostic signal generating unit and outputs the diagnostic signal. An example of the filter characteristic of the filter unit is a high-pass filter. According to the diagnostic device configured in this manner, it is possible to easily avoid the phenomenon in which the diagnostic signal is saturated, and it is possible to perform a diagnosis with high accuracy.

[0114] Moreover, the diagnostic device according to the fifth embodiment is the diagnostic device according to the first to third embodiments, further comprising a filter unit that reduces the resonance frequency components of the load device including the motor, the peripheral devices, and the load devices with respect to the diagnostic signal output by the diagnostic signal generating unit and outputs the reduced frequency component. An example of the filter characteristic of the filter unit is a notch filter. According to the diagnostic device configured in this manner, it is possible to prevent the characteristics of an abnormality in the load device and the characteristics of a resonance phenomenon that may occur in the load device from being mixed together, thereby providing the effect of enabling accurate diagnosis.

[0115] Embodiment 6 Fig. 21 is a diagram showing a configuration example of an electric equipment system 100E including a diagnostic device 2 according to a sixth embodiment. Compared with the electric equipment system 100D shown in Fig. 17, the configuration of the filter unit 26 is specified in Fig. 21. Specifically, the filter unit 26 includes a subtractor 261 and a notch filter 262. The roles of the diagnostic signal generating unit 23, the sine wave signal generating unit 24, the product-sum calculating unit 22 and the determining unit 21 are the same as those of the first to fifth embodiments described above.

[0116] The notch filter 262 generates a diagnostic signal τ' by reducing the resonant frequency components of the load device including the motor 3, the peripheral device 6, and the load device 7 from the diagnostic signal τ output by the diagnostic signal generation unit 23, and outputs the generated diagnostic signal τ' to the product-sum calculation unit 22. The diagnostic signal τ and the diagnostic signal τ' are also input to the subtractor 261. The subtractor 261 subtracts the diagnostic signal τ from the diagnostic signal τ', generates a differential signal τ" representing the difference between the two, and outputs the differential signal τ" to the judgment unit 21. This differential signal τ" can be expressed by the following equation (10).

[0117]

number

[0118] In the above equation (10), G(s) is a transfer function that represents the filter characteristics of the notch filter 262.

[0119] The differential signal τ" is a signal that contains the characteristics of the resonance phenomenon. For example, if there is a chip in a tooth in the reducer, the resonance frequency components are expected to increase. For this reason, the differential signal τ" can be used to capture such trends and to utilize them in diagnosing the load device. In this document, the diagnostic signal τ' will sometimes be called the "first signal" and the differential signal τ" the "second signal."

[0120] Fig. 22 is a diagram for explaining the effect of the filter section 26 according to embodiment 6. Fig. 22 shows, from the top, the diagnostic signal τ before application of the filter section 26, the diagnostic signal τ' after application of the filter section 26, and the differential signal τ'', in that order.

[0121] Looking at the diagnostic signal τ before applying the filter unit 26, the signal amplitude increases at about 0.5 (s) due to the resonance phenomenon, but by applying the filter unit 26, the characteristics of the resonance phenomenon can be removed, as can be seen in the diagnostic signal τ'. Also, looking at the differential signal τ'', it can be seen that only the characteristics of the resonance phenomenon have been extracted.

[0122] As described above, the diagnostic device according to the sixth embodiment is the diagnostic device according to the first to third embodiments, further including a filter unit that outputs a first signal in which the resonance frequency components of the load device including the motor, the peripheral devices, and the load devices are reduced, and a second signal in which the resonance frequency components are emphasized, for the diagnostic signal output by the diagnostic signal generating unit. An example of the filter characteristic of the filter unit is a notch filter. According to the diagnostic device configured in this manner, it is possible to prevent the characteristics of an abnormality in the load device and the characteristics of a resonance phenomenon that may occur in the load device from being mixed together, thereby providing the effect of enabling accurate diagnosis.

[0123] Embodiment 7 Fig. 23 is a diagram showing a first configuration example of an electric equipment system 100F including a diagnostic device 2 according to the seventh embodiment. Compared with the electric equipment system 100 shown in Fig. 2, in Fig. 23, the determination unit 21 in Fig. 2 is replaced with an abnormality part identification unit 27. The abnormality part identification unit 27 has a function of identifying an abnormal part in a load device including the motor 3, the peripheral device 6, and the load device 7. The roles of the diagnostic signal generation unit 23, the sine wave signal generation unit 24, and the product-sum calculation unit 22 are the same as those of the first to sixth embodiments described above, but new functions are added to the sine wave signal generation unit 24 and the product-sum calculation unit 22.

[0124] FIG. 24 is a diagram showing a second configuration example of an electric device system 100F including a diagnostic device 2 according to the seventh embodiment. In FIG. 24, a coefficient array change unit 28 is further added to the configuration of FIG. 23. FIG. 25 is a diagram showing a third configuration example of an electric device system 100F including a diagnostic device 2 according to the seventh embodiment. In FIG. 25, an input device 42 is further added to the configuration of FIG. 24. As shown in FIG. 25, the diagnostic device 2 configured as an independently operating device may be combined with the input device 42 to configure a diagnostic system 40. That is, the diagnostic system 40 includes the input device 42 connected to the diagnostic device 2 via a transmission line, and the coefficient array change unit 28 is configured to change the coefficient array based on information on a coefficient to be added to the coefficient array or a coefficient to be deleted from the coefficient array from the input device 42.

[0125] 26 is a diagram showing a fourth configuration example of an electric device system 100F including the diagnostic device 2 according to the embodiment 7. In Fig. 26, a fast Fourier transform unit 29 is further added to the configuration in Fig. 24.

[0126] In the first to fourth configuration examples shown in Figs. 23 to 26, the sine wave signal generating unit 24 generates a plurality of sine wave signals with different frequencies using a plurality of coefficients k and information on the motor position, and outputs the signals to the product-sum calculation unit 22. The product-sum calculation unit 22 calculates the product-sum of the diagnostic signal τ and the plurality of sine wave signals, and outputs a plurality of product-sum calculation results. The abnormal part identification unit 27 identifies the abnormal part in the load device based on the plurality of product-sum calculation results and fault characteristic information described later. As described above, the judgment unit 21 and the abnormal part identification unit 27 are not essential, and the plurality of product-sum calculation results may simply be output so that the user can make a judgment. This allows the presence or absence of an abnormality and identification of the abnormal part from the plurality of product-sum calculation results.

[0127] Specifically, the product-sum calculation unit 22 uses a coefficient array consisting of the above-mentioned multiple coefficients k to calculate the magnitudes m of multiple features and outputs the calculated magnitudes m to the abnormal part identification unit 27. The abnormal part identification unit 27 detects an abnormality based on the magnitudes m of multiple features and identifies the part where the abnormality occurs. For example, in Table 1, when the magnitude m of the feature calculated from the coefficient k corresponding to the bearing increases, it can be determined that an abnormality exists in the part where the magnitude m of the feature has increased.

[0128] The coefficient array consisting of a plurality of coefficients k and the fault characteristic information can be stored in the above-mentioned storage unit 702. The fault characteristic information is information that associates the coefficient k with a faulty part of the load device.

[0129] If the bearings used on the motor 3 side and the bearings used on the loading device 7 side have different structures and dimensions, the fault characteristic information will also be different. Therefore, by referring to the fault characteristic information stored in the storage unit 702, it is possible to identify and specify whether the bearing of the motor 3 or the loading device 7 has an abnormality.

[0130] A representative method for setting the coefficient array is to use a sequence with equal intervals. For example, if the interval is 0.1, the array of the coefficient k is (0, 0.1, 0.2, 0.3, ...). The narrower the interval, the more precisely the feature magnitude m can be calculated, but the amount of calculation increases. To reduce the amount of calculation, for example, as shown in Table 1, if the order of the abnormal feature frequency corresponding to the part to be diagnosed can be known in advance, only the order and the values ​​before and after the order may be stored as the coefficient k. In this case, the order that does not correspond to the part to be diagnosed may be excluded from the coefficient array. The change of the coefficient array including this operation, that is, the exchange of information on the coefficient to be added to the coefficient array or the coefficient to be deleted from the coefficient array, is performed through the coefficient array change unit 28. The change of the coefficient array may be performed by a person through an operation panel or a network, or may be performed by a communication process between another diagnostic device 2, a higher-level controller of the diagnostic device 2, or an input device connected to the diagnostic device 2 through a transmission line. That is, the coefficient array change unit 28 may be an operation panel that can access the product-sum calculation unit 22, or may be a communication device that can communicate with the diagnostic device 2 or a higher-level controller of the diagnostic device 2, or an input device connected to the diagnostic device 2 via a transmission path.

[0131] As described above, the fourth configuration example in FIG. 26 includes a fast Fourier transform unit 29. Fast Fourier transform is useful as a representative method for frequency analysis that requires a small amount of calculation. However, this method is limited to cases where the frequency trend indicated by the target time series data does not change, and is not suitable for cases where the frequency indicating the characteristics of an abnormality changes due to acceleration / deceleration of the vehicle. The coefficient array change unit 28 proposed in this paper is useful as a countermeasure to this problem.

[0132] First, when the load device is operated at a constant speed or with a small rotational acceleration, the fast Fourier transform unit 29 performs a fast Fourier transform on the diagnostic signal τ. The fast Fourier transform unit 29 or the coefficient array change unit 28 learns orders in which abnormal characteristics are likely to appear. Either the fast Fourier transform unit 29 or the coefficient array change unit 28 determines that the orders in which abnormal characteristics appear should be monitored even during acceleration / deceleration. The coefficient array change unit 28 adds the orders in which abnormal characteristics appear and the vicinity of those orders to the coefficient array during acceleration / deceleration operation. By performing such processing, it becomes possible to quickly detect abnormalities even during acceleration / deceleration operation with limited computational processing capacity.

[0133] Fig. 27 is a diagram for explaining the effects of the coefficient array changing unit 28 and the fast Fourier transform unit 29 according to the seventh embodiment. Fig. 27 shows the result of the fast Fourier transform and the result of the product-sum operation after the coefficient array has been changed. The horizontal axis of Fig. 27 is the coefficient, and the vertical axis of Fig. 27 is the magnitude of the feature.

[0134] For example, if the coefficients are set at intervals of 0.2, the coefficient array will be (1.0, 1.2, 1.4, 1.6, 1.8, 2.0) in the range from 1.0 to 2.0. On the other hand, if abnormal characteristics appear in the coefficients 1.3 and 1.7 as shown in FIG. 27, this tendency cannot be detected. Therefore, when the load device is operated at a constant speed or with a small rotational acceleration, the coefficient array change unit 28 detects abnormal characteristics based on the fast Fourier transform result of the diagnostic signal τ. Here, if it is determined that an abnormal tendency is observed in the coefficients 1.3 and 1.7, the coefficient array change unit 28 adds the values ​​of the coefficients 1.3 and 1.7 and their surrounding values. In this way, this abnormal tendency can be detected even during acceleration and deceleration.

[0135] As described above, the diagnostic device according to the seventh embodiment includes a storage unit that stores a coefficient array consisting of a plurality of coefficients in the diagnostic devices according to the first to sixth embodiments. The sine wave signal generation unit generates and outputs a plurality of sine wave signals having different frequencies using a plurality of coefficients and motor position information. The product-sum calculation unit calculates the product-sum of the diagnostic signal and the plurality of sine wave signals, and outputs a plurality of product-sum calculation results. The diagnostic device configured in this manner has the effect of enabling highly accurate diagnosis to be performed using coefficients previously stored in the storage unit, without inputting a coefficient array.

[0136] The diagnostic device according to the seventh embodiment may further include an abnormality part identification unit that identifies an abnormal part in a load device including peripheral devices and load devices. The storage unit stores, in addition to the coefficients in the coefficient array, fault feature information that is information that associates the motor and the fault part of the load device, and the abnormality part identification unit identifies the abnormal part in the load device based on the results of multiple product-sum operations and the fault feature information. The diagnostic device configured in this way has the effect of being able to identify the abnormal part accurately and quickly.

[0137] The diagnostic device according to the seventh embodiment may further include a coefficient array change unit that adds or removes coefficients from the coefficient array. The coefficient array change unit may be configured to change the coefficient array by a person's operation via an operation panel or a network, or by communication processing between another diagnostic device, a higher-level controller of the diagnostic device, or an input device connected to the diagnostic device via a transmission line. Alternatively, the coefficient array change unit may be configured to change all of the intervals between the coefficients in the coefficient array, or to partially change some of the intervals. According to the diagnostic device configured in this way, it is possible to obtain the effect of freely adding and removing coefficients and changing the coefficient array.

[0138] The diagnostic device according to the seventh embodiment may further include a fast Fourier transform unit that performs a fast Fourier transform on the diagnostic signal. In this case, the coefficient array change unit changes the coefficient array using the result of the fast Fourier transform by the fast Fourier transform unit as an input. According to the diagnostic device configured in this manner, even when the load device is operated at a constant speed or with a small rotational acceleration, or when the load device is accelerating or decelerating, it is possible to capture the characteristics or tendency of an abnormality, and therefore it is possible to obtain an effect of identifying the part of the abnormality with high accuracy and more quickly.

[0139] The configurations shown in the above embodiments are merely examples, and may be combined with other known technologies, or the embodiments may be combined with each other. Also, parts of the configurations may be omitted or modified without departing from the spirit of the invention. [Explanation of symbols]

[0140] 1 Power conversion device, 2 Diagnostic device, 3 Motor, 4 Motor current detection unit, 5 Position sensor, 6 Peripheral device, 7 Load device, 8 Electrical wiring, 9 Vibration noise observation unit, 10 Power conversion unit, 11 Voltage command value calculation unit, 12 Inverter, 13 UVW / dq conversion unit, 21 Judgment unit, 22 Product-sum calculation unit, 23 Diagnostic signal generation unit, 24 Sine wave signal generation unit, 25 Window function multiplication unit, 26 Filter unit, 27 Abnormal part identification unit, 28 Coefficient array change unit, 29 Fast Fourier transform unit, 40 Diagnostic system, 42 Input device, 50 Motor position calculation unit, 100, 100A, 100B, 100C, 100D, 100E, 100F Electrical equipment system, 261 Subtractor, 262 Notch filter, 701 Processor, 702 Memory unit, 703 Processing circuitry, 704 interface.

Claims

1. In a diagnostic device for diagnosing the status of a motor, the status of peripheral equipment connected to the motor, or the status of load equipment connected to the motor, A diagnostic signal generation unit generates a diagnostic signal that includes at least one of the following pieces of information: a detected value of the motor current, which is the current flowing through the motor; a motor current command value, which is the command value of the motor current; a torque command value, which is the command value of the motor torque applied to the motor; a motor torque expressed as a function of the motor current; the acceleration of the motor; the speed of the motor; and a disturbance torque applied to the motor as a disturbance. A sine wave signal generation unit takes the motor position, which is the position information of the motor, as input and uses a value obtained by multiplying the motor position by a coefficient for diagnosis to generate a sine wave signal whose frequency is a function of the coefficient that fluctuates in proportion to the coefficient in response to the speed fluctuations of the motor. A sum-of-products unit that calculates the sum of products of the diagnostic signal and the sinusoidal signal, A storage unit that stores a coefficient array consisting of multiple coefficients, A coefficient array modification unit that adds or removes coefficients from the coefficient array, The system includes a fast Fourier transform unit that performs a fast Fourier transform on the diagnostic signal, The sine wave signal generation unit generates and outputs multiple sine wave signals of different frequencies using a plurality of coefficients and motor position information. The sum-of-products unit calculates the sum of the products of the diagnostic signal and the plurality of sinusoidal signals and outputs a plurality of sum-of-products calculation results. The coefficient array modification unit modifies the coefficient array using the result of the Fast Fourier Transform performed by the Fast Fourier Transform unit as input. A diagnostic device characterized by the following features.

2. In a diagnostic device for diagnosing the status of a motor, the status of peripheral equipment connected to the motor, or the status of load equipment connected to the motor, A diagnostic signal generation unit takes an estimated motor position, which is the position information of the motor, as input and generates a diagnostic signal that includes at least one of the following pieces of information: a detected motor current, which is the current flowing through the motor; a motor current command value, which is the command value of the motor current; a torque command value, which is the command value of the motor torque applied to the motor; a motor torque expressed as a function of the motor current; the acceleration of the motor; the speed of the motor; and a disturbance torque applied to the motor as a disturbance. A sinusoidal signal generation unit takes the estimated value of the motor position as input and generates a sinusoidal signal whose frequency is a function of the coefficient that fluctuates proportionally to the coefficient in accordance with the speed fluctuations of the motor, using a value obtained by multiplying the motor position by a coefficient for diagnosis. A sum-of-products unit that calculates the sum of products of the diagnostic signal and the sinusoidal signal, A storage unit that stores a coefficient array consisting of multiple coefficients, A coefficient array modification unit that adds or removes coefficients from the coefficient array, The system includes a fast Fourier transform unit that performs a fast Fourier transform on the diagnostic signal, The sine wave signal generation unit generates and outputs multiple sine wave signals of different frequencies using a plurality of coefficients and motor position information. The sum-of-products unit calculates the sum of the products of the diagnostic signal and the plurality of sinusoidal signals and outputs a plurality of sum-of-products calculation results. The coefficient array modification unit modifies the coefficient array using the result of the Fast Fourier Transform performed by the Fast Fourier Transform unit as input. A diagnostic device characterized by the following features.

3. In a diagnostic device for diagnosing the status of a motor, the status of peripheral equipment connected to the motor, or the status of load equipment connected to the motor, A diagnostic signal generation unit generates a diagnostic signal that includes information on observed noise or vibration values ​​of the motor, A sine wave signal generation unit takes the motor position, which is the position information of the motor, as input and uses a value obtained by multiplying the motor position by a coefficient for diagnosis to generate a sine wave signal whose frequency is a function of the coefficient that fluctuates in proportion to the coefficient in response to the speed fluctuations of the motor. A sum-of-products unit that calculates the sum of products of the diagnostic signal and the sinusoidal signal, A storage unit that stores a coefficient array consisting of multiple coefficients, A coefficient array modification unit that adds or removes coefficients from the coefficient array, The system includes a fast Fourier transform unit that performs a fast Fourier transform on the diagnostic signal, The sine wave signal generation unit generates and outputs multiple sine wave signals of different frequencies using a plurality of coefficients and motor position information. The sum-of-products unit calculates the sum of the products of the diagnostic signal and the plurality of sinusoidal signals and outputs a plurality of sum-of-products calculation results. The coefficient array modification unit modifies the coefficient array using the result of the Fast Fourier Transform performed by the Fast Fourier Transform unit as input. A diagnostic device characterized by the following features.

4. The system includes an abnormality identification unit that identifies the location of an abnormality in the load device, including the peripheral equipment and the load equipment. The storage unit stores fault characteristic information, which is information that associates the coefficient with the location of the failure in the motor and the load device. The abnormal location identification unit identifies the location of the abnormality in the load device based on the multiple sum-of-accumulate calculation results and the fault characteristic information. A diagnostic device according to any one of claims 1 to 3.

5. The coefficient array modification unit adds to the coefficient array the coefficient at which the amplitude of the vertical axis becomes abnormal when the horizontal axis is set to coefficients related to frequency in the result of the fast Fourier transform. The diagnostic device according to feature 1.

6. A diagnostic device according to any one of claims 1 to 3, and an input device connected to the diagnostic device via a transmission line, The coefficient array modification unit is configured to modify the coefficient array based on information from the input device regarding coefficients to be added to the coefficient array or coefficients to be removed from the coefficient array. A diagnostic system characterized by the following features.

7. A diagnostic device according to any one of claims 1 to 3, A power conversion unit that controls the drive of the motor, A power conversion device characterized by comprising the following features.

8. A diagnostic method for diagnosing the condition of a motor, the condition of peripheral equipment connected to the motor, or the condition of load equipment connected to the motor, The first step is to detect the motor current, which is the current flowing through the motor, A second step of generating a diagnostic signal that includes at least one of the following pieces of information: the motor current, the motor current command value which is the command value of the motor current, the torque command value which is the command value of the motor torque applied to the motor, the motor torque expressed as a function of the motor current, the acceleration of the motor, the speed of the motor, and the disturbance torque applied to the motor as a disturbance. A third step involves taking the motor position, which is the position information of the motor, as input, and using a value obtained by multiplying the motor position by a coefficient for diagnosis, to generate a sinusoidal signal whose frequency is a function of the coefficient that fluctuates in proportion to the coefficient in response to the speed fluctuations of the motor. A fourth step involves calculating the sum of products of the diagnostic signal and the sinusoidal signal, A fifth step involves storing a coefficient array consisting of multiple coefficients, A sixth step of adding or removing coefficients from the coefficient array, A seventh step involves performing a fast Fourier transform on the diagnostic signal, The eighth step includes modifying the coefficient array using the result of the Fast Fourier Transform performed in the seventh step as input, The third step includes generating and outputting a plurality of sinusoidal signals with different frequencies using a plurality of coefficients and motor position information. The fourth step includes calculating the sum of products of the diagnostic signal and the plurality of sinusoidal signals and outputting a plurality of sum of product calculation results. A diagnostic method characterized by the following features.

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