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

Abstract

A diagnostic device (2) comprises: a diagnostic signal generation unit (23) that generates a diagnostic signal including information pertaining to at least one of a detected value of a motor current, which is a current flowing through a motor (3), a motor current command value, which is a command value of the motor current, a torque command value, which is a command value of a motor torque that is applied to the motor (3), a motor torque represented as a function of the motor current, an acceleration of the motor (3), a speed of the motor (3), and a disturbance torque applied as a disturbance to the motor (3); a sine wave signal generation unit (24) that takes a motor position, which is position information of the motor (3), as input and uses a value obtained by multiplying a coefficient for diagnosis by the motor position to generate a sine wave signal having a frequency that is a function varying in proportion to the coefficient depending on speed fluctuations of the motor (3); and a product sum calculation unit (22) that calculates the sum of products of the diagnostic signal and the sine wave signal.

Description

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

[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.

[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, the impact of the failure may also cause abnormalities in unintended locations, potentially extending the failure period. Examples of load devices mentioned here include XY stages, linear guides, and robot arms. Examples of peripheral devices include couplings, reducers, and bearings.

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

[0004] Several conventional techniques related to predictive maintenance are known. A first conventional technique involves frequency analysis of time-series data obtained by monitoring the status of a device, detecting signs of abnormality in the device based on the analyzed frequency domain information, and identifying the cause of the abnormality. However, this technique assumes 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 typically operates by repeatedly accelerating and decelerating. Therefore, this technique, which assumes operation at a constant rotation speed, is not suitable for diagnosing devices that drive servo motors.

[0005] A second conventional technique involves operating the device using a dedicated diagnostic operating pattern, separate from normal operation, and determining the device's status based on the data obtained during that operation. However, this technique has the drawback of reducing productivity because normal operation is not possible when the device is operating using the dedicated diagnostic operating pattern. Furthermore, if the dedicated diagnostic operating pattern differs significantly from the operating pattern during normal operation, it is difficult or impossible to detect signs of abnormalities during normal operation.

[0006] A third conventional technique is a technique for 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 discloses a technique 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 that uses the rotational position of the input shaft or the output shaft of the rotating device as a variable, and extracting frequency components related to the rotation of the input shaft or the output shaft of the rotating device based on the fitting results.

[0007] The technology of Patent Document 1 is characterized by its ability 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 basis functions, is treated as a problem of parameter estimation using the least squares method.

[0008] Least squares methods can be broadly divided into batch least squares methods, which process input and output data in a batch to estimate parameters, and recursive least squares methods, which process input and output data sequentially to estimate parameters. While batch least squares methods require the calculation of an inverse matrix, there are issues with the computational load involved in calculating an inverse matrix, and with the possibility that an inverse matrix may not exist. For this reason, it is considered unsuitable to use batch least squares methods for this type of fitting calculation. While it is unclear whether this issue has been taken into consideration, Patent Document 1 uses the recursive least squares method. In addition to least squares methods, other methods known for fitting to basis functions include batch gradient descent and stochastic gradient descent.

[0009] The recursive least squares method will be briefly explained. The recursive least squares method sequentially calculates an 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 although the parameter is often expressed as θ, ψ will be used here.

[0010]

[0011] In the above equations (1) and (2), ψ^(t) and ψ^(t-1) are estimated values ​​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 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.

[0012] JP 2015-034776 A

[0013] As mentioned above, Patent Document 1 uses the recursive least squares method for the calculation process related to fitting to basis functions. However, the recursive least squares method has the problem that if the initial values ​​of the covariance matrix and the forgetting factor are inappropriate, the calculation takes a long time to converge and the parameter estimation process may diverge. Similar problems are unavoidable not only with the recursive least squares method but also with other fitting methods. For example, the batch gradient descent method has the problem of a heavy calculation load, and the stochastic gradient descent method has the problem of being easily affected by noise and may not result in convergence. Furthermore, both methods have the problem that if the value of the learning rate, which represents the weight for the gradient, is not set appropriately, the convergence takes a long time and the parameter estimation process may diverge.

[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 view of the above, and aims to provide a diagnostic device that can perform highly accurate diagnosis while shortening the time required for calculation processing.

[0016] 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 generator, a sine wave signal generator, and a product-sum calculator. The diagnostic signal generator generates a diagnostic signal including at least one of information on a detected motor current value, which is the current flowing through the motor, a motor current command value, which is a command value for the motor current, a torque command value, which is a command value for the motor torque to be applied to the motor, motor torque expressed as a function of the motor current, motor acceleration, motor speed, and disturbance torque applied to the motor as a disturbance. The sine wave signal generator receives a motor position, which is position information of the motor, and uses a value obtained by multiplying the motor position by a diagnostic coefficient to generate a sine wave signal whose frequency is a function of a frequency that varies in proportion to the coefficient in response to fluctuations in motor speed. The product-sum calculator calculates the product-sum of the diagnostic signal and the sine wave signal.

[0017] The diagnostic device according to the present disclosure has the effect of being able to perform a highly accurate diagnosis while reducing the calculation processing time.

[0018] FIG. 1 is a diagram showing a configuration example of an electrical equipment system including a diagnostic device according to embodiment 1. FIG. 2 is a diagram showing another configuration example of an electrical equipment system including a diagnostic device according to embodiment 1. FIG. 3 is a diagram showing a configuration example when the diagnostic device according to embodiment 1 is configured as a standalone device. FIG. 4 is a diagram showing a configuration example of a product-sum calculation unit and a sine wave signal generation unit according to embodiment 1. FIG. 5 is a diagram showing another configuration example of a product-sum calculation unit and a sine wave signal generation unit according to embodiment 1. FIG. 6 is a diagram showing an example of a processing result by the diagnostic device according to embodiment 1. FIG. 7 is a diagram used to explain the timing of current detection in embodiment 1. FIG. 8 is a diagram used to explain the timing of motor position detection in embodiment 1. FIG. 1 shows a first example of a window function in embodiment 4. FIG. 2 shows a second example of a window function in embodiment 4. FIG. 3 shows an example of a processing result by a diagnostic device according to embodiment 4. FIG. 4 shows an example of a configuration of an electrical equipment system including a diagnostic device according to embodiment 5. FIG. 5 shows an example of a filter characteristic of a filter section according to embodiment 5. This figure is provided for explaining the main points of other features of the diagnostic device according to embodiment 5. This figure shows an example of a filter characteristic suitable for a filter section according to embodiment 5. This figure shows an example of a configuration of an electrical equipment system including a diagnostic device according to embodiment 6. This figure is provided for explaining the effect of the filter section according to embodiment 6. This figure shows a first example of a configuration of an electrical equipment system including a diagnostic device according to embodiment 7. This figure shows a second example of a configuration of an electrical equipment system including a diagnostic device according to embodiment 7. This figure shows a third example of a configuration of an electrical equipment system including a diagnostic device according to embodiment 7. This figure shows a fourth example of a configuration of an electrical equipment system including a diagnostic device according to embodiment 7. This figure is provided for explaining the effect of a coefficient array change unit and a fast Fourier transform unit according to embodiment 7.

[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] First Embodiment. FIG. 1 is a diagram illustrating an example configuration of an electrical equipment system 100 including a diagnostic device 2 according to a first embodiment. In FIG. 1, the electrical equipment system 100 is configured with a power conversion device 1 and a diagnostic device 2. FIG. 1 illustrates an example configuration in which the diagnostic device 2 is provided external to the power conversion device 1, which supplies operating power to the motor 3. Note that, although FIG. 1 illustrates the motor 3 as a three-phase motor connected to a three-phase UVW power line, the motor 3 is not limited to a three-phase motor. The motor 3 may be a single-phase motor or a polyphase motor having four or more phases. In this document, the motor 3 will be described as a three-phase motor, as an example. The motor 3 may perform either rotary motion or linear motion. The latter case is specifically referred to as a linear motor. In this document, the term "motor" is used to include a "linear motor."

[0021] Furthermore, a load device 7, which is an object to be rotated or moved linearly, is connected to the motor 3 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 a limitation. Some of these components may be integrated into one 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] The electric wiring 8 connecting the power conversion device 1 and the motor 3 is provided with a motor current detector 4 for detecting 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 current transformer (CT), 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 perform detection using a shunt resistor.

[0024] In FIG. 1, the motor current detectors 4 are arranged in the U-phase and V-phase, but not in the W-phase. However, the W-phase current i ｗ is 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 unit 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 the motor position calculation unit 50 of the diagnostic device 2. If the motor 3 rotates, the motor position corresponds to the rotation angle θ (rad) indicating the rotational 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 made assuming that the motor position is the rotation angle θ (rad).

[0026] The motor position calculation unit 50 calculates the rotation angle θ based on the detected value of the motor position output from the position sensor 5. The rotation angle θ calculated by the motor position calculation unit 50 is input to the diagnostic signal generation unit 23 and the sine wave signal generation unit 24. Inside the diagnostic signal generation unit 23, a three-phase current i, which is a current signal on the three-phase coordinate system, is calculated according to the following equation (3): ｕ , i ｖ , i ｗ is the d-axis current i, which is the current signal on the dq-axis rotation coordinate system. ｄ and q-axis current i ｑ is converted to

[0027]

[0028] In the above equation (3), p represents the number of pole pairs. ｕ , i ｖ , i ｗ is 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 will be described later. The estimated value of the rotation angle θ obtained by estimation calculation is called the "estimated rotation angle," and is represented by θ^. In this case, the above equation (3) can be expressed as the following equation (4) using the estimated rotation angle θ^.

[0030]

[0031] The diagnostic signal generator 23 generates the rotation angle θ or the 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, as highly efficient and high-power motors, attention has been drawn to surface permanent magnet synchronous motors (SPMSMs) that utilize magnet torque and interior permanent magnet synchronous motors (IPMSMs) that utilize both magnet torque and reluctance torque. The motor torque, which is the torque output by a motor, is expressed as a function of 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 the estimated rotation angle θ^, which is an estimate of the rotation angle θ, and the q-axis current i ｑ The diagnostic signal τ is generated based on the q-axis current i ｑ 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 is 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 velocity, and further integrating the velocity 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 a single piece of information, but may include multiple pieces of information.

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

[0035] Furthermore, 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 is represented by d, the disturbance torque d can be estimated and calculated using the following equation (5).

[0036]

[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 that includes 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]

[0041] In the above equation (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 will be described later. In this paper, the two signals calculated according to the above equation (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 diagram showing an example of the characteristics of a bearing. The bearing is an example of the peripheral device 6.

[0043]

[0044] If there is an abnormality such as a scratch on the 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 that appear in the outer ring, cage, and rolling elements, which are the parts of the bearing, and their orders. 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. ｒ is expressed as f with respect to the motor speed ω, which is the rotation speed of the motor 3. ｒ As shown in Table 1, the abnormal characteristic frequency is ｒ The order can be expressed as a function of the abnormal feature frequency f ｒ The order is the value divided by the rotation frequency f ｒ By adopting this order for k in the above-mentioned equation (6), it becomes possible to identify and detect abnormalities in the bearing.

[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 the value obtained by multiplying the rotation angle θ by a diagnostic coefficient k to generate a sine wave signal having a frequency that varies in proportion to the coefficient k in accordance with 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 calculating 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 operation.

[0047]

[0048] In the above formula (7), N is the number of data points, and the subscript i means the signal at time i. ｋ As shown in the third equation, m can be calculated as the square root of the sum of the square of the value a calculated by the first equation and the square of the value b calculated by the second equation. ｋ It is not necessary to use the third formula to calculate, for example, omitting the square root calculation, ｋ = a ｋ ２ +b ｋ ２ The division by the number of data points N is done by the feature size m ｋ is a value that does not depend on 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) is regarded as a pseudo-integral operation, and the value a at time n-1 is calculated as in the following equation (8): k (n-1), the value a at time n k A sequential calculation formula using a low pass filter (LPF) may be used to obtain (n).

[0050]

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

[0052] The magnitude m of the feature 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 presence or absence of an abnormality is determined based on the feature size m ｋ is learned, and the size of the input feature mｋ is the size of the learned feature m ｋ If the characteristic deviation from the trend is found, it can be determined that an abnormality exists. 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 this configuration is not limiting. The diagnostic device 2 may also be provided inside the power conversion device 1, as shown in FIG. 2 . FIG. 2 is a diagram showing another example configuration of an electrical 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 using the diagnostic device 2. When the power conversion device 1 is configured to have a diagnostic function, there is an advantage that detected values ​​and the like used by the power conversion device 1 can be directly used for the diagnostic function, making signal handling easier. 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 dedicated sensor for the diagnostic function.

[0054] 2, the power conversion device 1 includes the diagnostic device 2 described in FIG. 1 inside. The power conversion device 1 also includes a power conversion unit 10 that controls the driving of the 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 motor position calculation unit 50 described in FIG. 1. The UVW / dq conversion unit 13 converts the three-phase current i on the three-phase coordinate system detected by the motor current detection unit 4 into a voltage command value. ｕ , i ｖ , i ｗ The d-axis current i on the dq-axis rotation coordinate system ｄ and q-axis current i ｑ This calculation requires information on the rotation angle θ, and therefore 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 iｑ and 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 originally provided in the power conversion unit 10 can be utilized.

[0057] 2, the diagnostic signal generator 23 receives the rotation angle θ and the q-axis current i ｑ Alternatively, only one of the signals may be input depending on the contents of the diagnostic signal τ. ｑ In addition to both, the d-axis current i ｄ The diagnostic signal generating unit 23 may be provided 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. ｑ When directly converting this into the diagnostic signal τ, there is no need to generate or calculate a new signal in the diagnostic signal generation unit 23. In this case, the diagnostic signal generation unit 23 is unnecessary, and the function of the diagnostic signal generation unit 23 can be substituted by the UVW / dq conversion unit 13. Also, in FIG. 2, information on the motor position is acquired from the position sensor 5, but as will be described in a third embodiment below, an estimated rotation angle θ^, which is an estimated value of the rotation angle θ, may be used instead of the rotation angle θ.

[0059] 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 standalone device. Furthermore, the diagnostic device 2 may be configured to operate while attached to a device other than the power conversion device.

[0060] 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 plurality of coefficients k are used instead of one coefficient k. Each of the plurality of coefficients k is k １ , k ２ , ..., and the corresponding feature size m ｋ m １ , m ２ , .... The coefficient k １ Based on the value a １ , b １ is calculated, and the value a １ , b １ Based on the feature size m １ is calculated. Also, the coefficient k ２ Based on the value a ２ , b ２ is calculated, and the value a ２ , b ２ Based on the feature size m ２ is calculated.

[0061] 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 the first embodiment. As shown in FIG. 5, the feature magnitude m １ , m ２ Value a for calculating １ , b １ and the value a ２ , b ２ 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. The top of FIG. 6 shows the motor speed (rpm), and the upper center of FIG. 6 shows the waveform of the sine wave signal generated by the sine wave signal generating unit 24. Furthermore, the lower center of FIG. 6 shows, with respect to the diagnostic signal generated by the diagnostic signal generating unit 23, a waveform under normal conditions indicated by a dashed line and a waveform under abnormal conditions indicated by a solid line. Furthermore, the bottom of FIG. 6 shows, with respect to the product-sum operation output output by the product-sum operation unit 22, a waveform under normal conditions indicated by a dashed line and a waveform under abnormal conditions indicated by a solid line. The horizontal axis represents time in seconds (s).

[0063] Here, we will explain an example of monitoring and diagnosing the condition of a servo motor and bearings. Servomotors accelerate and decelerate in short periods of less than one second. Accurately calculating the degree of abnormality from data collected over such a short period requires fast convergence to the true value. Furthermore, the detected value of the motor current may be affected by friction torque caused by the bearings inside the servo motor due to 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, these noise components must be removed during processing by the diagnostic 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, variations will occur in the monitoring results of the servo motor, which operates in a short time. If the variations are large, it becomes difficult to appropriately set a threshold value for distinguishing between normal and abnormal, and there is a risk of erroneously determining 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 that indicate abnormal states of the load devices, including the motor 3, peripheral devices 6, and load devices 7, and therefore cause an increase in 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] 7 is a diagram illustrating the timing of current detection in embodiment 1. FIG. 7 shows the operation of sampling the current at the timing of the peaks and valleys of a triangular wave carrier signal. Note that a triangular wave is an example of a carrier signal, and any waveform other than a triangular wave may be used as long as the signal waveform amplitude varies periodically with time.

[0068] If the motor 3 is a three-phase motor, the voltage applied to the motor 3 will be for three phases, UV and W, but for ease of explanation, only the U phase is shown in Figure 7. At the top of Figure 7, the waveform of the carrier signal is shown in a solid line, and the U phase modulated wave is shown in a dashed line. Also, at the top center of Figure 7, the waveform of the U phase voltage applied to the motor 3 is shown in a solid line, and at the bottom center of Figure 7, the waveform of the U phase current flowing through the motor 3 is shown in a solid line. Also, at the bottom of Figure 7, the waveform of the q-axis current converted by the UVW / dq converter 13 is shown in 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] Although the circuit configuration of the inverter 12 is not shown in FIG. 2, as is well known, the three-phase inverter 12 is configured such that 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 now be described. First, the U-phase modulated wave and the carrier signal are compared. When the modulated wave is greater 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, when 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 at frequencies near multiples of the carrier frequency. However, by detecting the motor current at the peaks or valleys of the carrier signal, as shown in FIG. 7, an average value can be detected with the influence of the pulsating components reduced. The motor current may be detected at both the peaks and valleys, or, for example, only at the valleys, or only at the peaks. Alternatively, the average value of the values ​​detected at the peaks and valleys may be used as the detected motor current value.

[0071] Fig. 8 is a diagram illustrating the timing of motor position detection in embodiment 1. The upper part of Fig. 8 shows the waveform of the carrier signal with a solid line, the center part of Fig. 8 shows the waveform of the q-axis current converted by the UVW / dq converter 13 with a dashed line, and the lower part of Fig. 8 shows the rotation angle representing the motor position with a two-dot chain line. The notations on the horizontal and vertical axes are the same as those in Fig. 7.

[0072] Figure 8 shows the operation of detecting motor position at the timing of the peaks and valleys of the carrier signal. The q-axis current, which is proportional to torque, changes the acceleration of the rotor or mover. Acceleration is double-integrated to obtain 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 will be 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 thus the pulsating component of the motor position is also suppressed. Furthermore, the smaller the moment of inertia and mass and the lower the carrier frequency, the more problematic the motor position pulsation becomes. However, by detecting the motor position 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 generator, a sine wave signal generator, and a product-sum calculator. The diagnostic signal generator generates a diagnostic signal including at least one of information on a detected motor current (current flowing through the motor), a motor current command value (a command value for the motor current), a torque command value (a command value for the motor torque to be applied to the motor), a motor torque expressed as a function of the motor current, motor acceleration, motor speed, and disturbance torque applied to the motor as a disturbance. The sine wave signal generator receives a motor position (position information of the motor) as an input and uses a value obtained by multiplying the motor position by a diagnostic coefficient to generate a sine wave signal whose frequency is a function of a frequency that varies in proportion to the coefficient in response to fluctuations in motor speed. The product-sum calculator calculates the product-sum of the diagnostic signal and the sine wave signal. When the diagnostic signal is used, it is difficult to accurately diagnose abnormality or normality using a threshold value. On the other hand, when a product-sum operation output obtained by multiplying and accumulating a diagnostic signal and a sine wave signal is used, it becomes easier to set the threshold, and therefore diagnostic accuracy can be improved compared to when only the diagnostic signal is used. Furthermore, since the product-sum operation output representing the magnitude of the feature can be obtained by sequential calculation without performing an inverse matrix calculation, the calculation processing time can be shortened. Therefore, the diagnostic device according to the first embodiment has the effect of enabling highly accurate diagnosis while shortening the calculation processing time.

[0074] Furthermore, 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 following first to fourth steps. In the first step, a motor current, which is a current flowing through the motor, is detected. In the second step, a diagnostic signal is generated, which includes information on at least one of the motor current, a motor current command value, which is a command value for the motor current, a torque command value, which is a command value for the motor torque to be applied to the motor, motor torque expressed as a function of the motor current, motor acceleration, motor speed, and disturbance torque applied to the motor as a disturbance. In the third step, a motor position, which is position information of the motor, is input, and a sinusoidal signal is generated using a value obtained by multiplying the motor position by a diagnostic coefficient, the value being a function of a frequency that varies in proportion to the coefficient in response to motor speed fluctuations. In the fourth step, a product sum of the diagnostic signal and the sinusoidal signal is calculated. As with the diagnostic device according to the first embodiment, the diagnostic method according to the first embodiment has the effect of enabling high-precision 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 generator be a value detected at the timing of the peak or valley of the carrier signal. Similarly, it is desirable that the motor position be a value detected at the timing of the peak or valley of the carrier signal. If the motor current and motor position are detected at the timing of the peak or valley of the carrier signal, it is possible to suppress pulsating components that may be included in the motor current and motor position, which has the effect of enabling further improvement in diagnostic accuracy.

[0076] The motor to be diagnosed may not only be a motor that receives electrical energy and outputs mechanical energy, but also a generator that receives mechanical energy and outputs electrical energy. Furthermore, the motor to be diagnosed may not only be 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-described method. Furthermore, it is not essential to provide a determination unit; the user may obtain the results of the product-sum calculation and determine whether or not there is an abnormality.

[0077] At the end of the first embodiment, a description will be given of a hardware configuration for realizing the functions of the above-described 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] When realizing 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 computing unit. The processor 701 may be a computing unit called a microprocessor, a microcomputer, a microcontroller, a central processing unit (CPU), or a digital signal processor (DSP). Examples of the storage unit 702 include non-volatile or volatile semiconductor memory such as a random access memory (RAM), a read-only memory (ROM), a flash memory, an erasable programmable read-only memory (EPROM), or an electrically programmable read-only memory (EEPROM), a magnetic disk, a flexible disk, an optical disk, a compact disk, a minidisk, and a digital versatile disk (DVD).

[0080] The storage unit 702 stores a program that executes the functions of the diagnostic device 2 according to embodiment 1. The processor 701 exchanges 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-described processing. The calculation results by the processor 701 can be stored in the storage unit 702.

[0081] Furthermore, when realizing the functions of the diagnostic device 2 according to the first embodiment, 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 functions 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 may be a single circuit, a composite circuit, an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a combination thereof. Information input to and output from the processing circuit 703 can be exchanged via an interface 704. Furthermore, the processing results by the processing circuit 703 can be stored in a storage unit 702.

[0082] It is also possible that some of the processing in the diagnostic device 2 is performed by the processing circuit 703 and other processing that is not performed by the processing circuit 703 is performed by the processor 701 .

[0083] Embodiment 2. Fig. 11 is a diagram showing an example of the configuration of an electric equipment system 100A including a diagnostic device 2 according to embodiment 2. Compared to the electric equipment system 100 shown in Fig. 1, in Fig. 11, the motor 3 is provided with a vibration and noise observation unit 9 that observes the vibration or noise of the motor 3. Also, in Fig. 11, the motor current detection unit 4 that was provided in the electrical wiring 8 is not shown. Note that the omission of the motor current detection unit 4 means that the detected value of the motor current detection unit 4 is not used in the processing of the diagnostic device 2 according to embodiment 2, but 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 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 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 determination unit 21 have the same roles as in the first embodiment. 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, rather than the current or torque.

[0085] 11 shows the diagnostic device 2 as a device provided outside the power conversion device 1, but the present invention 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 detected values ​​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 that diagnoses 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 generator, a sine wave signal generator, and a product-sum calculator. The diagnostic signal generator generates a diagnostic signal including information on observed values ​​of motor noise or vibration. The sine wave signal generator 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 diagnostic coefficient to generate a sine wave signal whose frequency is a function of a frequency that varies in proportion to the coefficient in response to speed fluctuations of the motor. The product-sum calculator calculates the product-sum of the diagnostic signal and the sine wave signal. Similar to the diagnostic device according to the first embodiment, the diagnostic device according to the second embodiment has the effect of enabling high-precision diagnosis while reducing the calculation processing time.

[0087] Furthermore, a 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 following first to fourth steps. In the first step, noise or vibration of the motor is observed. In the second step, a diagnostic signal containing information on the observed value of the motor noise or vibration is generated. In the third step, a motor position, which is position information of the motor, is input, and a sine wave signal is generated using a value obtained by multiplying the motor position by a diagnostic coefficient to generate a sine wave signal whose frequency is a function of a frequency that varies in proportion to the coefficient depending on motor speed fluctuations. In the fourth step, a product sum of the diagnostic signal and the sine wave signal is calculated. Similar to the diagnostic device according to the second embodiment, the diagnostic method according to the second embodiment has the effect of enabling high-precision diagnosis while shortening the calculation processing time.

[0088] Third Embodiment. FIG. 12 is a diagram illustrating a configuration example of an electric equipment system 100B including a diagnostic device 2 according to a third embodiment. Compared to the electric equipment system 100 illustrated in FIG. 2 , the position sensor 5 provided on the motor 3 is eliminated in FIG. 12 . While the motor position calculator 50 inputs the rotation angle θ indicating the motor position to the diagnostic signal generator 23 and the sine wave signal generator 24 in FIG. 2 , the motor position calculator 50 inputs an estimated rotation angle θ^, which is an estimate of the rotation angle θ, to the diagnostic signal generator 23 and the sine wave signal generator 24 in FIG. 12 . The roles of the diagnostic signal generator 23, the sine wave signal generator 24, the product-sum calculator 22, and the determiner 21 are the same as those in the first and second embodiments. Using the estimated rotation angle θ^ eliminates the need for the position sensor 5 and wiring between the position sensor 5 and the diagnostic device 2, thereby reducing the manufacturing cost of the device. Furthermore, since there is no need to consider failure of the position sensor 5, the reliability of the device is 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 also 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 generator, a sine wave signal generator, and a product-sum calculator. The diagnostic signal generator receives an estimated motor position value, which is motor position information, as input, and generates a diagnostic signal including at least one of information on a detected motor current, which is a current flowing through the motor, a motor current command value, which is a command value for the motor current, a torque command value, which is a command value for the motor torque to be applied to the motor, motor torque expressed as a function of the motor current, motor acceleration, motor speed, and disturbance torque applied to the motor as a disturbance. The sine wave signal generator receives an estimated motor position value as input, and uses a value obtained by multiplying the motor position by a diagnostic coefficient to generate a sine wave signal whose frequency is a function of a frequency that varies in proportion to the coefficient in response to motor speed fluctuations. Similar to the diagnostic devices according to the first and second embodiments, the diagnostic device according to the third embodiment achieves the effect of enabling high-precision diagnosis while shortening the calculation processing time.

[0091] Furthermore, a diagnostic method according to a 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 following first to fourth steps. In the first step, a motor current, which is a current flowing through the motor, is detected. In the second step, an estimated motor position value, which is motor position information, is input, and a diagnostic signal is generated, which includes at least one of information on the detected motor current, a motor current command value, which is a command value for the motor current, a torque command value, which is a command value for the motor torque to be applied to the motor, motor torque expressed as a function of the motor current, motor acceleration, motor speed, and disturbance torque applied to the motor as a disturbance. In the third step, the estimated motor position value is input, and a sinusoidal signal is generated, using a value obtained by multiplying the estimated motor position value by a diagnostic coefficient, with the result that the frequency varies in proportion to the coefficient in response to motor speed fluctuations. In the fourth step, a product sum of the diagnostic signal and the sinusoidal signal is calculated. According to the diagnostic method of embodiment 3, similar to the diagnostic methods of embodiments 1 and 2, it is possible to obtain the effect of being able to perform a highly accurate diagnosis while shortening the calculation processing time.

[0092] Fourth Embodiment Fig. 13 is a diagram showing an example of the configuration of an electric equipment system 100C including a diagnostic device 2 according to a fourth embodiment. Compared to 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 in the first to third embodiments described above.

[0093] The window function multiplication unit 25 receives the diagnostic signal τ generated by the diagnostic signal generation unit 23 and the rotation angle θ output from the motor position calculation unit 50. The window function multiplication unit 25 corrects the diagnostic signal τ using the rotation angle θ, and outputs the corrected diagnostic signal τ' to the 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 represents the motor speed, and the vertical axis represents the magnitude of the correction coefficient by which the diagnostic signal τ is multiplied. N １, N ２ , N ３ , N ４ is a predetermined setting value. Examples of the setting value will be described later.

[0095] 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 of signal saturation caused by the diagnostic signal τ being close to a constant value, as will be described later.

[0096] 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. ５ is N ２ is a positive velocity with an absolute value greater than N ６ is N ４ It is a negative velocity with a larger absolute value than

[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 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 to prevent this pulsation from being input to the product-sum calculation unit 22 and affecting the calculation processing of the product-sum calculation unit 22.

[0098] 14 and 15 show examples of 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 no window function is 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 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 Figs. 6 and 16 are both the results of calculations using the above-mentioned formula (8), i.e., a calculation formula using an LPF. The calculation result in Fig. 16 is obtained by using the window function shown in Fig. 14 with N １ = 100 (rpm) 、N ２ =300 (rpm), N ３ =-100 (rpm), N ４ = -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). Furthermore, the value of the product-sum operation output is 0 on average in normal cases, and has a positive value only in abnormal cases. In this way, by using a window function, saturation can be easily avoided, enabling accurate diagnosis.

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

[0102] 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, but 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 embodiment 4 is the diagnostic device according to embodiments 1 to 3, further comprising a window function multiplier that multiplies the diagnostic signal by a window function that is a function of the motor speed or the motor acceleration, corrects the diagnostic signal, and outputs the corrected diagnostic signal. The diagnostic device according to embodiment 4 has the effect of easily avoiding the phenomenon of the diagnostic signal being saturated, thereby enabling accurate diagnosis. Note that the window function multiplier may be configured to multiply the sine wave signal by a window function that is a function of the motor speed or the motor acceleration, correct the sine wave signal, and output the corrected sine wave signal.

[0104] Fifth Embodiment Fig. 17 is a diagram showing an example of the configuration of an electric equipment system 100D including a diagnostic device 2 according to a fifth embodiment. Compared to 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 first to fourth embodiments described above.

[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 calculating unit 22.

[0106] Fig. 18 is a diagram showing an example of the filter characteristics of the filter unit 26 according to the fifth embodiment. The horizontal axis of Fig. 18 represents frequency, and the vertical axis represents gain. Fig. 18 shows the characteristics of a high-pass filter having a cutoff frequency that attenuates the low-frequency side of the frequency range. If a high-pass filter with such characteristics is used, the low-frequency components of the diagnostic signal output from the diagnostic signal generating unit 23 are reduced before being output, thereby achieving the same effect as that of the diagnostic device 2 according to the fourth embodiment, which uses a window function.

[0107] 19 is a diagram illustrating another key feature of the diagnostic device 2 according to the fifth embodiment. The load device including the motor 3, peripheral device 6, and load device 7 has a concept of a resonance frequency that is determined by, for example, the rigidity of the coupling, belt, etc., and the moment of inertia around the rotation axis 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 does not depend on the motor speed, which is the rotation speed of the motor 3. Furthermore, in the above Table 1, for bearings, the abnormality characteristic frequency and the rotation frequency f corresponding to the motor speed are listed. ｒ explained the relationship between

[0108] Figure 19 shows the relationship between the kth frequency, which changes depending on the motor speed when a 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 ｒThe frequency obtained by multiplying k 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 FIG. 19. In FIG. 19, the line of the first frequency where k=1 is shown as a thick dashed line, and two lines of kth frequencies with two different k>1 and two lines of kth frequencies with two different k<1 are shown as dashed lines.

[0109] Since the line of the kth order frequency rises to the right, the kth order frequency and the resonant frequency coincide at a certain motor speed. In FIG. 19, the motor speed at which the kth order frequency and the resonant frequency coincide is expressed as ω １ , ω ２ , ω ３ , ω ４ The motor speeds ω １ , ω ２ , ω ３ , ω ４ In this case, for example, the bearing characteristics 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 diagnostic signal level due to the resonance phenomenon leads to a decrease in diagnostic accuracy.

[0110] FIG. 20 is a diagram illustrating an example of a filter characteristic suitable for the filter unit 26 according to the fifth embodiment. FIG. 20 illustrates a filter characteristic that attenuates components at 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, but not at other frequencies. Setting this center frequency equal to the resonant frequency can solve the above-described problem of a mixture of characteristics of an abnormality in the load device and characteristics of a resonance phenomenon that may occur in the load device. Such a filter characteristic G(s) can be expressed by the following equation (9):

[0111]

[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 ｎis the center frequency.

[0113] As described above, the diagnostic device according to embodiment 5 is the same as the diagnostic devices according to embodiments 1 to 3, except that it further includes a filter unit that reduces low-frequency components of the diagnostic signal output by the diagnostic signal generator and outputs the diagnostic signal. An example of the filter characteristics of the filter unit is a high-pass filter. The diagnostic device configured in this manner can easily avoid the phenomenon of diagnostic signal saturation, thereby achieving the effect of enabling highly accurate diagnosis.

[0114] Furthermore, 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 resonant frequency components of a load device, including a motor, peripheral devices, and load devices, from the diagnostic signal output by the diagnostic signal generator. An example of the filter characteristic of the filter unit is a notch filter. The diagnostic device configured in this manner can prevent the characteristics of an abnormality in the load device from being mixed with the characteristics of a resonance phenomenon that may occur in the load device, thereby achieving the effect of enabling accurate diagnosis.

[0115] Sixth Embodiment Fig. 21 is a diagram showing an example of the configuration 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, Fig. 21 shows a more specific configuration of the filter unit 26. Specifically, the filter unit 26 includes a subtractor 261 and a notch filter 262. The roles of the diagnostic signal generator 23, sine wave signal generator 24, product-sum calculator 22, and determiner 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 resonance frequency component of the load device including the motor 3, the peripheral device 6, and the load device 7, for 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 determination unit 21. This differential signal τ" can be expressed by the following equation (10).

[0117]

[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 of the reducer, it is expected that the resonance frequency component will increase. For this reason, the differential signal τ" can be used to capture such trends and can be used to diagnose the load device. In this paper, the diagnostic signal τ' will sometimes be called the "first signal" and the differential signal τ" will sometimes be called the "second signal."

[0120] 22 is a diagram illustrating the effect of the filter unit 26 according to the sixth embodiment. In FIG. 22, from the top, the diagnostic signal τ before application of the filter unit 26, the diagnostic signal τ′ after application of the filter unit 26, and the differential signal τ″ are shown in this order.

[0121] Looking at the diagnostic signal τ before applying the filter unit 26, the signal amplitude increases at approximately 0.5 (s) due to a 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 same as the diagnostic devices according to the first to third embodiments, except that the diagnostic device further includes a filter unit that outputs a first signal in which the resonant frequency components of a load device including a motor, peripheral devices, and load devices are reduced, and a second signal in which the resonant frequency components are emphasized, in relation to the diagnostic signal output by the diagnostic signal generator. An example of the filter characteristics of the filter unit is a notch filter. The diagnostic device configured in this manner can prevent the characteristics of an abnormality in the load device from being mixed with the characteristics of a resonance phenomenon that may occur in the load device, thereby enabling accurate diagnosis.

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

[0124] FIG. 24 is a diagram showing a second example configuration of an electric equipment system 100F including a diagnostic device 2 according to embodiment 7. In FIG. 24, a coefficient array modification unit 28 is further added to the configuration of FIG. 23. FIG. 25 is a diagram showing a third example configuration of an electric equipment system 100F including a diagnostic device 2 according to embodiment 7. In FIG. 25, an input device 42 is further added to the configuration of FIG. 24. As shown in FIG. 25, a diagnostic device 2 configured as a standalone device may be combined with the input device 42 to form a diagnostic system 40. That is, the diagnostic system 40 includes the input device 42 connected to the diagnostic device 2 via a transmission path, and the coefficient array modification unit 28 is configured to modify the coefficient array based on information from the input device 42 about coefficients to be added to or deleted from the coefficient array.

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

[0126] In the first to fourth configuration examples shown in Figures 23 to 26, the sine wave signal generation unit 24 uses multiple coefficients k and motor position information to generate multiple sine wave signals with different frequencies and outputs them to the product-sum calculation unit 22. The product-sum calculation unit 22 calculates the product of the diagnostic signal τ and the multiple sine wave signals and outputs multiple product-sum calculation results. The abnormality part identification unit 27 identifies the abnormal part in the load device based on the multiple product-sum calculation results and fault characteristic information described below. As mentioned above, the judgment unit 21 and the abnormality part identification unit 27 are not essential, and multiple product-sum calculation results may simply be output so that the user can make a judgment. This allows the multiple product-sum calculation results to contribute to determining whether an abnormality exists and identifying the abnormal part.

[0127] Specifically, the product-sum calculation unit 22 calculates the magnitudes m of the multiple features using a coefficient array consisting of the multiple coefficients k described above, 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 the multiple features, and identifies the part where the abnormality has occurred. For example, in Table 1, if 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 the 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 memory unit 702, it is possible to identify whether the bearing of the motor 3 or the loading device 7 has an abnormality.

[0130] A typical method for setting the coefficient array is to use a progression with equal intervals. For example, if the interval is 0.1, the coefficient k will be arranged as (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, if the order of the abnormal feature frequency corresponding to the part to be diagnosed can be known in advance, as shown in Table 1, only that order and the values ​​before and after that order can be stored as the coefficient k. In this case, orders that do not correspond to the part to be diagnosed may be excluded from the coefficient array. The change of the coefficient array, including this operation, i.e., the exchange of information on coefficients to be added to or 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 human operation on an operation panel or via a network, or by communication processing between another diagnostic device 2, a higher-level controller of the diagnostic device 2, or an input device connected to the diagnostic device 2 via 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, 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 mentioned above, the fourth configuration example in FIG. 26 includes a fast Fourier transform unit 29. Fast Fourier transform is a useful method for frequency analysis that requires a small amount of calculation. However, this method is only applicable when the frequency trend indicated by the time series data in question remains constant, and is not suitable for cases where the frequency characteristic of an abnormality changes due to acceleration or deceleration of the vehicle. The coefficient array change unit 28 proposed in this paper is useful for addressing this issue.

[0132] First, when the load device is operated at a constant speed or with 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 modification unit 28 learns orders in which abnormal characteristics are likely to appear. Either the fast Fourier transform unit 29 or the coefficient array modification unit 28 determines that orders in which abnormal characteristics appear should be monitored even during acceleration / deceleration. The coefficient array modification unit 28 adds the order in which abnormal characteristics appear and its neighboring orders to the coefficient array during acceleration / deceleration. This processing makes it possible to quickly detect abnormalities even during acceleration / deceleration, even with limited computational processing capacity.

[0133] Fig. 27 is a diagram illustrating the effects of the coefficient array modification unit 28 and the fast Fourier transform unit 29 according to the seventh embodiment. Fig. 27 shows the results of the fast Fourier transform and the results of the product-sum operation after the coefficient array has been modified. The horizontal axis of Fig. 27 represents the coefficient, and the vertical axis of Fig. 27 represents 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, as shown in FIG. 27 , if abnormality characteristics appear at coefficients 1.3 or 1.7, this tendency cannot be detected. Therefore, when the load device is operated at a constant speed or with low rotational acceleration, the coefficient array modification unit 28 detects abnormality characteristics based on the results of a fast Fourier transform of the diagnostic signal τ. Here, if it is determined that an abnormal tendency is observed at coefficients 1.3 or 1.7, the coefficient array modification unit 28 adds the values ​​of coefficients 1.3, 1.7, and their surrounding values. This makes it possible to detect this abnormal tendency even during acceleration / deceleration.

[0135] As described above, the diagnostic device according to embodiment 7 includes a storage unit that stores a coefficient array consisting of a plurality of coefficients in the diagnostic devices according to embodiments 1 to 6. The sine wave signal generation unit generates and outputs a plurality of sine wave signals with different frequencies using the plurality of coefficients and motor position information. The product-sum operation unit performs product-sum operation on the diagnostic signal and the plurality of sine wave signals, and outputs a plurality of product-sum operation results. The diagnostic device configured in this manner has the advantage of enabling highly accurate diagnosis 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 the part of the abnormality in the load device, including the peripheral devices and the load devices. The storage unit stores the coefficients in the coefficient array as well as fault characteristic information, which is information that associates the fault parts of the motor and the load device, and the abnormality part identification unit identifies the part of the abnormality in the load device based on the results of the multiple product-sum operations and the fault characteristic information. A diagnostic device configured in this manner has the effect of being able to identify the part of the abnormality accurately and quickly.

[0137] The diagnostic device according to the seventh embodiment may further include a coefficient array modification unit that adds or removes coefficients from the coefficient array. The coefficient array modification unit may be configured to modify the coefficient array through a manual operation on an operation panel or via a network, or through 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 modification unit may be configured to modify all of the spacing between coefficients in the coefficient array, or to modify only some of the spacing. A diagnostic device configured in this manner provides the advantage of being able to freely add or remove coefficients and modify the coefficient array.

[0138] Furthermore, 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 performed by the fast Fourier transform unit as an input. With a diagnostic device configured in this manner, it is possible to capture the characteristics or tendency of an abnormality even when the load device is operating at a constant speed or with a small rotational acceleration, or even when the load device is accelerating or decelerating, thereby achieving the effect of identifying the location of the abnormality more accurately and quickly.

[0139] The configurations shown in the above embodiments are merely examples, and may be combined with other known technologies, or different embodiments may be combined with each other. It is also possible to omit or modify parts of the configurations as long as they do not deviate from the gist of the invention.

[0140] REFERENCE SIGNS LIST 1 Power conversion device, 2 Diagnostic device, 3 Motor, 4 Motor current detection unit, 5 Position sensor, 6 Peripheral equipment, 7 Load equipment, 8 Electrical wiring, 9 Vibration and noise observation unit, 10 Power conversion unit, 11 Voltage command value calculation unit, 12 Inverter, 13 UVW / dq conversion unit, 21 Determination 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 circuit, 704 interface.

Claims

1. 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, comprising: a diagnostic signal generation unit that generates a diagnostic signal including at least one of information on a detected motor current value that is the current flowing through the motor, a motor current command value that is a command value for the motor current, a torque command value that is a command value for the motor torque to be applied to the motor, motor torque expressed as a function of the motor current, the acceleration of the motor, the speed of the motor, and disturbance torque applied to the motor as a disturbance; a sine wave signal generation unit that receives as input a motor position that is position information of the motor, and uses a value obtained by multiplying the motor position by a diagnostic coefficient to generate a sine wave signal whose frequency is a function of a frequency that varies in proportion to the coefficient in response to speed fluctuations of the motor; and a product-sum calculation unit that calculates the product-sum of the diagnostic signal and the sine wave signal.

2. The diagnostic device according to claim 1, characterized in that the motor current is controlled by a pulse width modulated signal generated using a carrier signal that is a function of a carrier frequency, and the motor current input to the diagnostic signal generating unit is a value detected at the timing of the peak or valley of the carrier signal.

3. The diagnostic device according to claim 1, characterized in that the motor current is controlled by a pulse width modulated signal generated using a carrier signal that is a function of a carrier frequency, and the motor position is a value detected at the timing of a peak or valley of the carrier signal.

4. 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, comprising: a diagnostic signal generation unit that receives an estimated motor position value, which is position information of the motor, as an input, and generates a diagnostic signal including at least one of information on a detected motor current value, which is the current flowing in the motor, a motor current command value, which is a command value for the motor current, a torque command value, which is a command value for the motor torque to be applied to the motor, motor torque expressed as a function of the motor current, the acceleration of the motor, the speed of the motor, and disturbance torque applied to the motor as a disturbance; a sine wave signal generation unit that receives the estimated motor position value as an input, and uses a value obtained by multiplying the motor position by a diagnostic coefficient to generate a sine wave signal whose frequency is a function of a frequency that varies in proportion to the coefficient in response to speed fluctuations of the motor; and a product-sum calculation unit that calculates the product-sum of the diagnostic signal and the sine wave signal.

5. A diagnostic device according to any one of claims 1 to 4, further comprising a window function multiplication unit that multiplies the diagnostic signal by a window function that is a function of the speed or acceleration of the motor, corrects the diagnostic signal, and outputs the corrected diagnostic signal.

6. A diagnostic device according to any one of claims 1 to 4, further comprising a window function multiplication unit that multiplies the sine wave signal by a window function that is a function of the speed or acceleration of the motor, corrects the sine wave signal, and outputs the corrected signal.

7. A diagnostic device according to any one of claims 1 to 4, further comprising a filter section that reduces low frequency components of the diagnostic signal output by the diagnostic signal generation section and outputs the signal.

8. A diagnostic device according to any one of claims 1 to 4, further comprising a filter section that reduces the resonant frequency components of a load device including the motor, the peripheral equipment, and the load equipment from the diagnostic signal output by the diagnostic signal generation section and outputs the reduced frequency component.

9. A diagnostic device according to any one of claims 1 to 4, further comprising a filter unit that outputs a first signal in which the resonant frequency components of a load device including the motor, the peripheral equipment, and the load equipment are reduced, and a second signal in which the resonant frequency components are emphasized, in relation to the diagnostic signal output by the diagnostic signal generation unit.

10. A diagnostic device for diagnosing the state of a motor, the state of peripheral equipment connected to the motor, or the state of load equipment connected to the motor, comprising: a diagnostic signal generation unit that generates a diagnostic signal containing information on observed values ​​of noise or vibration of the motor; a sine wave signal generation unit that receives motor position, which is position information of the motor, as an input, and uses a value obtained by multiplying the motor position by a diagnostic coefficient to generate a sine wave signal whose frequency is a function of a frequency that varies in proportion to the coefficient in accordance with speed fluctuations of the motor; and a product-sum calculation unit that calculates the product of the diagnostic signal and the sine wave signal.

11. A diagnostic device as claimed in any one of claims 1 to 10, characterized in that it comprises a memory unit that stores a coefficient array consisting of a plurality of said coefficients, wherein the sine wave signal generation unit uses a plurality of said coefficients and information on the motor position to generate and output a plurality of said sine wave signals with different frequencies, and the product-sum calculation unit calculates the product-sum of the diagnostic signal and a plurality of said sine wave signals and outputs a plurality of product-sum calculation results.

12. A diagnostic device according to claim 11, further comprising an abnormality part identification unit that identifies an abnormal part in a load device including the peripheral equipment and the load equipment, wherein the memory unit stores failure characteristic information that is information that associates the coefficient with a faulty part in the motor and the load device, and the abnormality part identification unit identifies an abnormal part in the load device based on a plurality of the product-sum calculation results and the failure characteristic information.

13. The diagnostic device according to claim 11 or 12, further comprising a coefficient array modification unit that adds or removes coefficients from the coefficient array.

14. The diagnostic device according to claim 13, wherein the coefficient array change unit is configured to be able to change all of the intervals between the coefficients in the coefficient array, or to change some of the intervals partially.

15. A diagnostic device according to claim 13 or 14, characterized in that it comprises a fast Fourier transform unit that performs a fast Fourier transform on the diagnostic signal, and the coefficient array change unit changes the coefficient array using the result of the fast Fourier transform performed by the fast Fourier transform unit as an input.

16. A diagnostic system comprising the diagnostic device according to any one of claims 13 to 15 and an input device connected to said diagnostic device via a transmission line, wherein said coefficient array modification unit is configured to modify said coefficient array based on information from said input device about coefficients to be added to or deleted from said coefficient array.

17. A power conversion device comprising: a diagnostic device according to any one of claims 1 to 15; and a power conversion unit that controls the driving of the motor.

18. 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, comprising: a first step of detecting a motor current that is a current flowing through the motor; a second step of generating a diagnostic signal containing at least one piece of information from the motor current, a motor current command value that is a command value for the motor current, a torque command value that is a command value for the motor torque to be applied to the motor, motor torque expressed as a function of the motor current, the acceleration of the motor, the speed of the motor, and disturbance torque applied to the motor as a disturbance; a third step of using a motor position that is position information of the motor as an input and multiplying the motor position by a diagnostic coefficient to generate a sine wave signal whose frequency is a function of a frequency that varies in proportion to the coefficient in response to speed fluctuations of the motor; and a fourth step of calculating the sum of products of the diagnostic signal and the sine wave signal.

19. A diagnostic method for diagnosing the state of a motor, the state of peripheral equipment connected to the motor, or the state of load equipment connected to the motor, comprising: a first step of observing the noise or vibration of the motor; a second step of generating a diagnostic signal containing information on the observed value of the noise or vibration of the motor; a third step of using the motor position, which is position information of the motor, as an input and multiplying the motor position by a diagnostic coefficient to generate a sine wave signal whose frequency is a function of the frequency that varies in proportion to the coefficient in accordance with speed fluctuations of the motor; and a fourth step of calculating the sum of products of the diagnostic signal and the sine wave signal.

20. 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, comprising: a first step of detecting a motor current that is a current flowing through the motor; a second step of taking an estimated motor position value that is motor position information as input and generating a diagnostic signal containing at least one of information on the detected motor current value, a motor current command value that is a command value for the motor current, a torque command value that is a command value for the motor torque to be applied to the motor, motor torque expressed as a function of the motor current, the acceleration of the motor, the speed of the motor, and disturbance torque applied to the motor as a disturbance; a third step of taking the estimated motor position value as input and using a value obtained by multiplying the estimated motor position value by a diagnostic coefficient to generate a sine wave signal whose frequency is a function of a frequency that varies in proportion to the coefficient in accordance with speed fluctuations of the motor; and a fourth step of calculating the sum of products of the diagnostic signal and the sine wave signal.

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