Diagnostic device and diagnostic method for rotating machine system

Abstract

To provide a technique capable of precisely diagnosing conditions such as abnormalities related to the power transmission mechanism even when the load of the motor and rotating machine fluctuates while suppressing or preventing false diagnosis, etc.SOLUTION: A rotating machine system 100 includes: an electric motor 3 that is driven based on the electric power supplied from a power source; a power transmission mechanism 6; a rotary machine 4; and a diagnostic device 10 that is connected to a power line from the power source to the electric motor 3. The power transmission mechanism 6 has a first pulley 3P at the electric motor 3 side, a second pulley 4P at the rotary machine 4 side, and a drive transmission part such as belt 5. The diagnostic device 10 is configured so as to, when the load applied to the rotary machine 4 from the electric motor 3 via the power transmission mechanism 6 fluctuates, detect a current in the power, normalize the current value on the time series of the detected current at predetermined time intervals, determine at least the normal or abnormal state as the state of the power transmission mechanism 6 by using current data after normalization, and store / output the diagnosis result.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a condition diagnosis technique for a rotating machine system. [Background technology]

[0002] A rotating machine system drives a rotating machine by transmitting driving force from an electric motor (in other words, a motor) based on AC power through a power transmission mechanism including a belt, etc. For example, there are many belt-driven rotating machines, such as air compressors, injection molding machines, and fans.

[0003] In a rotating machine system, when a power transmission mechanism deteriorates or becomes abnormal, the transmission of driving force becomes insufficient or abnormal. For this reason, technology that can diagnose and detect the condition of the power transmission mechanism is being studied.

[0004] An example of prior art related to the condition diagnosis of a rotating machine system is International Publication No. 2018 / 109993 (Patent Document 1). Patent Document 1 describes, as an "abnormality diagnosis device for a power transmission mechanism," that the abnormality diagnosis device includes an abnormality diagnosis unit that determines an abnormality in the power transmission mechanism and a current detector connected to the power supply of the electric motor, and that the abnormality diagnosis unit includes an analysis unit that analyzes the current transmitted from the current detector and an abnormality determination unit that determines an abnormality in the power transmission mechanism from the analysis result of the analysis unit. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] International Publication No. 2018 / 109993 Summary of the Invention [Problem to be solved by the invention]

[0006] In order to ensure the performance and safety of the rotating machinery, the condition of the power transmission mechanism, including the belt, in the rotating machinery system needs to be properly managed through regular maintenance, etc. However, compared to other equipment, equipment that operates continuously for a long time or has a complex mechanism has a relatively high downtime cost due to regular maintenance.

[0007] In an example of Patent Document 1 relating to the condition diagnosis of a power transmission mechanism, it is described that the current value obtained from a current detector is subjected to frequency analysis, and the number of sideband wave peaks other than the rotation frequency of the motor that exceed a threshold value is detected, thereby determining whether an abnormality has occurred in the power transmission mechanism.

[0008] Conventionally, there has been a technique for diagnosing the state of a power transmission mechanism in a rotating machine assuming a certain load, such as the example in Patent Document 1. In such a technique, the state of the power transmission mechanism is determined using a set constant threshold value.

[0009] However, in a rotating machine connected to an electric motor and a power transmission mechanism, when the load for driving the rotating machine fluctuates, it is difficult to determine the state of the rotating machine using a constant threshold value, and a misdiagnosis or a false alarm may occur. More specifically, when the load of the electric motor changes, the frequency spectrum peak value of the electric information of the electric motor also changes. As a result, a method of determining the state of the rotating machine by comparing the electric information with a constant threshold value may cause a misdiagnosis or a false alarm due to a false determination. A false determination may be a determination that an abnormality exists when the machine is normal, or a determination that an abnormality exists when the machine is normal. Therefore, it is difficult to apply the conventional state diagnosis technology to a rotating machine system in which the load changes from moment to moment.

[0010] An object of the present invention is to provide a technology for diagnosing the condition of a rotating machine system, which can diagnose abnormal conditions and other conditions related to a power transmission mechanism with high accuracy while suppressing or preventing erroneous diagnosis, even when there are fluctuations in the load on an electric motor and a rotating machine. [Means for solving the problem]

[0011] A representative embodiment of the present invention has the following configuration: A diagnostic device for a rotating machine system according to one embodiment is a diagnostic device for a rotating machine system including an electric motor driven based on electric power supplied from a power source, a power transmission mechanism connected to the electric motor, a rotating machine connected to the power transmission mechanism, and a diagnostic device connected to the electric motor or a power line from the power source to the electric motor and configured to diagnose a state of the power transmission mechanism, the power transmission mechanism having a first pulley that outputs a rotational driving force of the electric motor, a second pulley that drives the rotating machine, and a drive transmission component that connects the first pulley and the second pulley, and when a load applied from the electric motor to the rotating machine through the power transmission mechanism varies, the diagnostic device detects a current in the electric power, normalizes a current value on a time series of the detected current at predetermined time intervals, determines a state of the power transmission mechanism using the normalized current data, and stores and outputs a diagnosis result. Effect of the Invention

[0012] According to a representative embodiment of the present invention, with regard to a condition diagnosis technology for a rotating machine system, even when there are fluctuations in the load on the electric motor and the rotating machine, it is possible to diagnose conditions such as abnormalities related to the power transmission mechanism with high accuracy while suppressing or preventing erroneous diagnosis, etc. [Brief description of the drawings]

[0013] [Figure 1] 1 is a diagram showing a configuration of a rotating machine system including a diagnosis device according to a first embodiment of the present invention. [Diagram 2] FIG. 2 is a diagram illustrating an example of the configuration of a diagnostic device in the first embodiment. [Diagram 3] FIG. 2 is a diagram showing a flow of main processes of the diagnostic device in the first embodiment. [Figure 4] FIG. 4 is a diagram showing detected current data and current information in the first embodiment. [Diagram 5] FIG. 4 is a diagram showing envelope detection and current normalization in the first embodiment. [Figure 6]FIG. 4 is a diagram showing an example of a result of frequency analysis of a current in the first embodiment. [Figure 7] FIG. 2 is a diagram showing an example of a display screen in the first embodiment. [Figure 8] FIG. 4 is an explanatory diagram showing an example of a load fluctuation in the first embodiment. [Figure 9] FIG. 11 is a diagram illustrating an example of determination in the case of a load fluctuation, in a comparative example of the first embodiment. [Figure 10] FIG. 11 is a diagram illustrating an example of determination in the case of a change in load in the first embodiment. [Figure 11] FIG. 11 is a diagram showing a flow of main processes performed by a diagnostic device according to a second embodiment of the present invention. [Figure 12] FIG. 11 is a diagram showing an example of a display screen in the second embodiment. [Figure 13] FIG. 13 is a diagram showing the Mahalanobis distance in the third embodiment of the present invention. [Figure 14] FIG. 11 is a diagram showing the configuration of a rotating machine system including a diagnostic device according to a fourth embodiment of the present invention. [Figure 15] FIG. 11 is a diagram showing the configuration of a rotating machine system including a diagnostic device according to a fifth embodiment of the present invention. [Figure 16] FIG. 13 is a diagram showing the configuration of a rotating machine system including a diagnostic device according to a sixth embodiment of the present invention. [Figure 17] FIG. 23 is a diagram showing an example of a spectral peak in the sixth embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. In addition, in principle, the same parts in all the drawings are given the same reference numerals, and repeated explanations will be omitted.

[0015] <Embodiment 1> A diagnostic device and a diagnostic method for a rotating machine system according to a first embodiment of the present invention will be described with reference to Figures 1 to 7. The diagnostic method according to the first embodiment is a method having steps executed by the diagnostic device according to the first embodiment.

[0016] [Rotary Mechanical Drive System] FIG. 1 shows a configuration of a rotating machine system 100 as a system including a diagnostic device 10 of the first embodiment. The rotating machine system 100 includes the diagnostic device 10 of the first embodiment, an AC power source 1, a switch 2, an electric motor 3, a power transmission mechanism 6, and a rotating machine 4. A portion 101 is configured by connections between the electric motor 3, the power transmission mechanism 6, and the rotating machine 4. The power transmission mechanism 6 is a belt drive transmission section, and has, as components, a first pulley 3P which is a motor-side pulley, a belt 5, and a second pulley 4P which is a rotating machine-side pulley. The rotating machine system 100 is configured of a main part by the AC power source 1, the switch 2, the electric motor 3, the power transmission mechanism 6, and the rotating machine 4, and the diagnostic device 10 is connected to the main part. The diagnostic device 10 may be disposed close to the electric motor 3, etc., or may be disposed remotely. A user U1 who is an operator operates and manages the diagnostic device 10, and checks the state of the rotating machine system 100, etc.

[0017] In the first embodiment, an example is shown in which the part 101 including the rotating machine 4 is applied to an air compressor, but the present invention is not limited to this and can be similarly applied to other types of rotating machine systems.

[0018] The AC power source 1 is a three-phase AC power source, and supplies power to the electric motor 3 via the switch 2. The switch 2 is a switch board that can switch between a power-on state (in other words, an on state) and a power-off state (in other words, an off state), and may include not only a switch circuit but also other control circuits and the like. The switch 2 is provided midway through a power line 1L that is connected between the AC power source 1 and the electric motor 3. The power line 1L is a line that transmits three-phase AC power. When starting operation of the rotating machine system 100, a user U1 operates the switch 2 to turn on the power. As a result, power from the AC power source 1 is supplied to the electric motor 3 via the power line 1L and the switch 2, and the electric motor 3 is rotated.

[0019] The electric motor 3 generates a rotational driving force for the rotating machine 4 as motive power based on electric power. For example, an induction motor is applied to the electric motor 3. However, the present invention is not limited to this, and various electric motors can be applied to the electric motor 3. The electric motor 3 is connected to the rotating machine 4 via a power transmission mechanism 6. The rotating machine 4 receives the rotational driving force of the electric motor 3 from the power transmission mechanism 6 and is rotationally driven.

[0020] The electric motor 3 has a first pulley 3P provided on a drive shaft (also called a motor shaft). The rotating machine 4 has a second pulley 4P provided on a drive shaft. Each pulley (3P, 4P) is a component that transmits power between the drive shaft and a belt 5. The belt 5 is an example of a power transmission component. The power transmission component is not limited to the belt 5, and a chain, a rope, or the like may be applied. The belt 5 is hung between the first pulley 3P and the second pulley 4P with friction or the like. One end of the belt 5 is hung on the first pulley 3P, and the other end is hung on the second pulley 4P. In this way, a belt drive transmission section (in other words, a winding transmission device) is configured by the pulleys (3P, 4P) and the belt 5.

[0021] The diagnostic device 10 is a device having a function of diagnosing the state of the power transmission mechanism 6, and can be implemented, for example, on a PC, an electronic circuit board, or the like. In the configuration example of Fig. 1, the diagnostic device 10 is connected to a power line 1L between an AC power source 1 and an electric motor 3. In particular, the diagnostic device 10 is connected to one point (one of the three-phase lines) on the power line 1L between the AC power source 1 and the switch 2 via a terminal 1N and a detection wire 1S.

[0022] The diagnostic device 10 has an electrical information detection unit 11, a diagnosis unit 13, a display device 14, a notification device 15, an operation unit 16, and a setting unit 17. A user U1 operates the operation unit 16 to input instructions and information to the diagnostic device 10. The operation unit 16 may be, for example, a keyboard, a mouse, an operation panel, or a PC or the like that is connected for communication.

[0023] The electric information detection unit 11 is connected to one point (one of the three-phase lines) of the power line 1L between the AC power source 1 and the electric motor 3 through the terminal 1N and the detection wire 1S. The electric information detection unit 11 is a part that detects, senses, or measures electric information of the power line 1L between the AC power source 1 and the electric motor 3, and is composed of a device such as a circuit. In the first embodiment, a current (phase current in this example) is used as the electric information. That is, in the first embodiment, the electric information detection unit 6 has a current sensor and can detect a current (in other words, current information, current value). The electric information detection unit 11 outputs a signal of the detected current as a current signal E1 to the diagnosis unit 13. The current signal E1 is an analog signal that represents a current value in a time series. Note that an analog-to-digital converter (ADC) is provided in the diagnosis device 10, for example, in the diagnosis unit 13 (or in the electric information detection unit 11). The analog signal that is the current signal E1 is appropriately converted into a digital signal by the ADC.

[0024] The diagnostic unit 13 is a part that diagnoses the state of the power transmission mechanism 6, particularly the state related to the drive transmission by the belt 5, based on the current signal E1, and performs processes such as storing and outputting the diagnosis results. In other words, the diagnostic unit 13 is a part that detects abnormalities, deterioration, or signs of abnormalities related to the transmission or drive force of the belt 5, and outputs a notification to the user U1, etc. A sign refers to a sign of an abnormality, failure, etc. before it actually occurs.

[0025] As a modified example, the diagnosing unit 13 may perform some kind of drive control on the part 101 including the electric motor 3 and the rotating machine 4 as output control based on the diagnosis result. For example, this control may include temporarily suspending the drive operation of the electric motor 3 or the rotating machine 4 or decelerating the operation when the diagnosis result indicates an abnormal state or a state with a high degree of degradation. In this case, a control signal is provided from the diagnosing device 10 to the switch 2, the electric motor 3, etc.

[0026] In a portion 101 configured with the electric motor 3, the power transmission mechanism 6, and the rotating machine 4, the current frequency (sometimes called electrical frequency, rotational frequency, etc.) of the electric motor 3 is fm [Hz], and the rotational frequency of the rotating machine 4 is fc [Hz]. The current frequency fm is a frequency corresponding to the rotational speed of the electric motor 3.

[0027] The current frequency fm can be known in advance from the frequency of the AC power supplied from the AC power source 1 to the electric motor 3. However, without being limited to this, a specific sensor may be provided in the electric motor 3 or between the AC power source 1 and the electric motor 3, and the current frequency fm may be detected by the sensor, or the current frequency fm may be calculated from sensor detection information.

[0028] In the first embodiment and the like, the rotating machine 4 is a rotating machine in which the load may fluctuate, and periodic load torque and rotation fluctuations may occur. Correspondingly, the load of the electric motor 3 may also fluctuate. In this rotating machine 4, the current waveform that changes with the current frequency fm of the electric motor 3 is observed as being modulated with the rotation frequency fc by the periodic load torque of the rotating machine 4. In other words, the amplitude of the current waveform at the current frequency fm of the electric motor 3 is modulated by the rotation frequency fc of the rotating machine 4.

[0029] In the first embodiment, the state of the power transmission mechanism 6, particularly the state related to the transmission of the belt 5, is diagnosed by distinguishing at least two states, "normal" and "abnormal." In terms of information processing, for example, the "normal" state is defined as a value of 0, and the "abnormal" state is defined as a value of 1. The "normal" state related to the transmission of the belt 5 corresponds to a case where no slippage or the like occurs between the first pulley 3P, the belt 5, and the second pulley 4P, or where no damage or the like occurs to the belt 5 or the like. The "abnormal" state corresponds to a case where slippage or the like occurs between the first pulley 3P, the belt 5, and the second pulley 4P, or where damage or the like occurs to the belt 5 or the like.

[0030] Without being limited thereto, the state of the power transmission mechanism 6 may be defined in more detail by three or more state values. For example, as the state value for the belt 5, a plurality of state values ​​representing the degree of deterioration between normal and abnormal may be used. For example, a state in which the degree of deterioration is small may be value 1, a state in which the degree of deterioration is medium may be value 2, and a state in which the degree of deterioration is large may be value 3. In this case, the diagnosis unit 13 uses a method for determining the degree of deterioration.

[0031] In addition to the "abnormal" state, a "premonitory" state may be used as a state value before the "abnormal" state. In this case, the diagnosis unit 13 uses a method for determining the premonitory state. The details of the method for determining the degree of deterioration and the premonitory state are not limited.

[0032] In the first embodiment, a current sensor is used to diagnose the condition, but the invention is not limited to this, and other types of sensors (such as a microphone) installed near the power transmission mechanism 6 may also be used in combination.

[0033] Assuming that the state of the belt 5 of the power transmission mechanism 6 is normal, the relationship between the current frequency fm and the rotation frequency fc can be expressed by the following formula 1. R1 is the diameter of the first pulley 3P. R2 is the diameter of the second pulley 4P. Rm is the number of pole pairs determined by the structure of the electric motor 3, and is a coefficient that converts between the rotation frequency of the mechanical motor shaft and the electrical frequency of the electric motor 3. Equation 1: fc=R2 / R1×fm / Rm

[0034] Based on the current signal E1 as electrical information acquired from the electrical information detection unit 11, the diagnosis unit 13 first calculates first information S1 as shown in the balloon in FIG. 1. The first information S1 is a state value that indicates the degree of abnormality in the transmission of the belt 5. The diagnosis unit 13 stores and outputs data including the first information S1 as a result of the diagnosis and detection. The diagnosis unit 13 displays, for example, the data including the first information S1 on the display screen of the display device 14. In this way, the state of the belt 5 of the power transmission mechanism 6 is conveyed to the user U1.

[0035] Furthermore, the diagnosis unit 3 determines second information S2 related to the diagnosis result and notification, etc., according to the first information S1, and controls the output of the notification device 15, etc., according to the value of the second information S2. That is, the diagnosis unit 13 gives a notification signal to the notification device 15 according to the value of the second information S2, thereby notifying the user U1 of a warning, etc. The notification device 15 issues a warning to inform the user U1 of an abnormal state of the belt 5, etc., according to the notification signal.

[0036] The diagnosis unit 13 determines a state value of the first information S1, for example, based on a comparison between a spectrum peak value, which will be described later, and a set threshold value (defined as H). For example, when the spectrum peak value is equal to or greater than the threshold value H, it is determined to be an abnormal state (in other words, a state in which the degree of abnormality is sufficiently high). Furthermore, the diagnosis unit 13 determines the second information S2 according to the state value of the first information S1.

[0037] The display device 14 is built into the diagnostic device 10 or is externally provided, and displays an image on a display screen. The diagnostic device 10 displays information on the display screen of the display device 14 using a predetermined graphical user interface. The notification device 15 is a device that realizes notification to the user U1 using voice, light emission, screen display, etc. The notification device 15 may be, for example, a buzzer device that generates a buzzer sound or the like and outputs it from a speaker, or a device that outputs an abnormal state from a speaker by synthetic voice. The notification device 15 may be a device that conveys a state by emitting a lamp, a device that displays a warning or the like on a display screen, or a device that notifies a destination of the state by email or the like. The notification device 15 and the display device 14 may be integrated.

[0038] The diagnostic device 10 diagnoses the state of the power transmission mechanism 6 while the part 101 including the electric motor 3, the power transmission mechanism 6, and the rotating machine 4 is in operation, i.e., during the time from the start to the end of operation. For example, the diagnostic device 10 can determine the operating state of the part 101 from the detection state of the electric information detection unit 11, and may control the start and end of the diagnosis in accordance with the operating state. Alternatively, the diagnostic device 10 may input information on the operating state from the electric motor 3, etc., and control the start and end of the diagnosis in accordance with the information. Alternatively, the diagnostic device 10 may control the start and end of the diagnosis based on the operation of the operation unit 16 by the user U1.

[0039] In the setting section 17, setting information such as a threshold value H related to the processing of the diagnosis section 13 can be set based on the user U1 checking the screen of the display device 14 and operating the operation section 16.

[0040] 1 of the first embodiment is a configuration example in which the diagnostic device 10 is installed externally to the part 101 including the rotating machine 4, etc., but as described later, this is not limited to this. The diagnostic device 10 may be implemented as being built into the electric motor 3, the switch 2, etc.

[0041] [Rotating Machinery] In other words, the portion 101 including the rotary machine 4 is an industrial machine that constitutes a manufacturing line or the like. A configuration example of a compressor, particularly an air compressor, as a specific example of the rotary machine 4 is as follows. There are various types of air compressors, and all of them are applicable. In the case of an air compressor, in the portion 101 of FIG. 1, the rotary machine 4 is composed of an air compression mechanism. Power generated by the electric motor 3 is transmitted to the air compression mechanism by the power transmission mechanism 6. The air compression mechanism includes an intake mechanism and a discharge mechanism. A rotor or a piston is provided inside the container of the air compression mechanism. Air sucked in from the intake mechanism is supplied to the container of the air compression mechanism, and the air is compressed by the air compression mechanism. The air compression mechanism compresses the air in the container by the rotational motion of the rotor or the reciprocating motion of the piston in the container based on the power transmitted from the power transmission mechanism 6. The air compressed by the air compression mechanism is discharged from the discharge mechanism. The compressed air discharged from the discharge mechanism is supplied through piping to a device that requires the compressed air (for example, an air cylinder or an air motor).

[0042] For example, the load on the air compressor may vary depending on the pressure of compressed air required for the device, etc. In response to the load variation, the current applied to the electric motor 3 and the rotational driving force applied from the electric motor 3 to the air compression mechanism, which is the rotating machine 4, through the power transmission mechanism 6 vary.

[0043] The applicable rotating machine 4 is not limited to an air compressor. The rotating machine 4 may be any machine that receives a rotational driving force from a power transmission mechanism 6. When the rotating machine 4 is, for example, a press machine, an example of the configuration is as follows. A press machine is a machine that processes a material such as metal by applying strong pressure and changes the shape based on a die. In the case of a press machine, in part 101 of FIG. 1, the rotating machine 4 is composed of a press mechanism. The power generated by the electric motor 3 is transmitted to the press mechanism by the power transmission mechanism 6. The press machine converts the rotational motion of the electric motor 3 into a reciprocating motion in the press mechanism through the power transmission mechanism 6. The press mechanism performs press processing on the supply member in the die by the reciprocating motion. For example, the load required by the press machine may vary depending on the processing content in the press mechanism. Depending on the load fluctuation, the current given to the electric motor 3 and the rotational driving force given to the press mechanism, which is the rotating machine 4, through the power transmission mechanism 6 vary.

[0044] [Diagnostic equipment] FIG. 2 shows an example of the hardware and software configuration of the diagnostic device 10. The diagnostic device 10 includes a processor 201, a memory 202, an auxiliary storage device 203, a communication interface device 204, a current sensor 205, a display device 14, and an alarm device 15, which are connected to each other via a bus or the like. The processor 201 is composed of a CPU, a ROM, a RAM, and the like, and is, in other words, a controller, and realizes a state diagnosis function based on the functional block configuration of FIG. 1 based on software program processing. A part of the processing may be realized by a dedicated circuit. The memory 202 or the auxiliary storage device 203 stores a control program 210, setting information 220, processing data 220, and the like. The auxiliary storage device 203 stores diagnostic result data 240, and the like. The processing data 220 and the diagnostic result data 240 include the first information S1 and the second information S2 of FIG. 1. The processing data 220 includes a spectrum analysis result, an envelope signal, a normalized signal, and the like, which are used or generated during processing and will be described later. The communication interface device 204 performs communication processing with the outside through a predetermined communication interface. The current sensor 205 constitutes the electric information detection unit 11 in FIG.

[0045] [Processing flow (1)] FIG. 3 shows a flow of main processes performed by diagnostic device 10, particularly by diagnostic unit 13. The flow in FIG. 3 has steps S101 to S114. In step S101, diagnostic unit 13 sets flag F for diagnostic operation. The value 0 of flag F means stop, and the value 1 means start. For example, when a user wants to start a diagnostic operation, flag F may be set to value 1 based on a button operation on operation unit 16. Alternatively, when diagnostic device 10 has a timer function, flag F may be automatically set to value 1 at preset time intervals using the timer function.

[0046] In step S102, if the value of flag F is 1 (i.e., start), the diagnostic unit 13 proceeds to step S103, and if the value is 0 (i.e., stop), the diagnostic unit 13 ends the flow. In step S103, the diagnostic unit 13 converts the analog signal, which is the current signal E1 from the electrical information detection unit 11, into a digital signal using an ADC. In other words, this digital signal is detected current data in a time series.

[0047] In step S104, the diagnostic unit 13 performs an operation of acquiring and collecting unit time data DU as data for each predetermined unit time TU (in other words, sampling time) based on the detected current data. The unit time data DU is, in other words, a sampling current value. The diagnostic unit 13 repeats this operation as an operation multiple times for each predetermined accumulation time (TL).

[0048] Fig. 4 shows the relationship between the detected current data and various times. In the graph of Fig. 4(A), the horizontal axis is time (time point t) and the vertical axis is current [A (ampere)]. Time 401 indicates a time consisting of accumulation times TL1, TL2, ..., TLm as a plurality (m) of accumulation times TL. Note that statistical values ​​such as an average, which will be described later, may be calculated in units of one accumulation time TL, or in units of a predetermined plurality (m) of accumulation times TL as one time 401.

[0049] The graph in FIG. 4B shows an enlargement of one accumulation time TL. One accumulation time TL includes a plurality of unit times TU. Current information Ir (in other words, a current value) is included as unit time data DU for each unit time TU. That is, one accumulation time TL includes a plurality of pieces of current information Ir, which are a plurality of unit time data DU. For each piece of data for each accumulation time TL, an envelope signal, a normalized signal, a spectrum peak, etc., which will be described later, are calculated.

[0050] In step S105, the diagnosis unit 13 accumulates each unit time data DU (current information Ir) during the accumulation time TL collected in step S104 in a memory (memory 202 in FIG. 2). In the time series, the process of collecting and accumulating data is repeated in the same manner for each accumulation time TL.

[0051] [Processing flow (2) - Envelope detection processing] In step S106, the diagnostic unit 13 performs an envelope detection process on the current information Ir for the accumulation time TL. Fig. 5 shows the envelope detection process in step S106. In step S106, the diagnostic unit 13 inputs the current information Ir, which is the unit time data DU accumulated in step S105, in a time series. The diagnostic unit 13 performs an envelope detection process based on the current information Ir and extracts it as an envelope signal IhL.

[0052] FIG. 5A shows an example of the envelope signal IhL. In the graph of FIG. 5A, the horizontal axis is time and the vertical axis is current. A line 501 is an example of the envelope signal IhL. FIG. 5A shows an envelope signal for data corresponding to one accumulation time TL of FIG. 4. Such an envelope signal IhL is a wave that changes periodically as shown in the figure, and rotation information on the rotating machine 4 side, particularly the rotation frequency fc, is expressed. By such envelope detection, the diagnosis unit 13 can extract rotation information on the rotating machine 4 side (i.e., information corresponding to the rotation frequency fc) from the detected current of the electric motor 3. The envelope signal IhL within the accumulation time TL has a maximum value and a minimum value. The maximum value is max(IhL) and the minimum value is min(IhL). As an example, a maximum value 502a and a minimum value 502b are shown.

[0053] In the example of the first embodiment, the diagnosis unit 13 performs heterodyne detection in the envelope detection of step S106. In heterodyne detection, the current information Ir is multiplied by a sine wave function that changes with the current frequency fm of the motor 3, as shown in the following equation 2. The diagnosis unit 13 generates the sine wave function using the current frequency fm of the motor 3. A multiplier in the diagnosis unit 13 multiplies the current information Ir by the sine wave function. The current information after the heterodyne detection of the current information Ir is set as post-detection current information Ih. Equation 2: Ih=Ir×sin(2π×fm×t)

[0054] Here, a description will be given assuming that the current information Ir follows the following formula 3. In formula 3, Io is the current amplitude. Also, from formula 3, formula 2 can be transformed into the following formula 4 using the half-angle formula. Equation 3: Ir={Io×sin(2π×fc×t)}×sin(2π×fm×t) Equation 4: Ih={Io×sin(2π×fc×t)}×(1-cos(2×2π×fm×t) / 2

[0055] The diagnosis unit 13 passes the post-detection current information Ih of Equation 4 through a low-pass filter (LPF) that removes the frequency component of 2×fm [Hz]. Then, the envelope signal IhL that changes with the rotation frequency fc of the rotating machine 4 is obtained by the following Equation 5. Equation 5: IhL={Io×sin(2π×fc×t)} / 2

[0056] As described above, the diagnosis unit 13 converts the current information Ir in FIG. 4 into an envelope signal IhL expressed by the rotation frequency fc as shown in FIG. 5(A) by the envelope detection process in step S106. In this example, heterodyne detection is applied in the envelope detection, but this is not limiting, and Hilbert transformation or the like can be similarly applied. Each calculation of the diagnosis unit 13 may be realized by software program processing or by a hardware circuit.

[0057] [Process flow (3)-Normalization process] 3, the diagnosis unit 13 performs normalization processing of the electrical information (particularly, current information). In step S107, the diagnosis unit 13 performs normalization processing (particularly, envelope signal normalization processing) on ​​the envelope signal IhL obtained in step S106 to obtain an envelope normalized signal Ist. The "normalization" used in the first embodiment is mathematical normalization, which is conversion to a value within a predetermined range (for example, a range from 0 to 1).

[0058] (B) of Fig. 5 shows an example of the envelope normalized signal Ist after the normalization process in step S107. A line 503 is an example of the envelope normalized signal Ist. The diagnosis unit 13 uses the following equation 6 to calculate the envelope normalized signal Ist. The envelope normalized signal Ist is a function of time (t) and is represented by Ist(t). The calculation of equation 6 is a normalization calculation in which the difference between the current value and the minimum value of the envelope signal IhL is divided by the difference between the maximum and minimum values ​​of the envelope signal IhL. Equation 6: Ist(t)={IhL(t)-min(IhL)} / {max(IhL)-min(IhL)}

[0059] 5A (line 501) is normalized to the envelope normalized signal Ist (line 503) in (B) by the calculation of Equation 6. In (B), the range 504 is the range from the minimum value 0 to the maximum value 1 in the normalization. In other words, the envelope normalized signal Ist is data normalized to the range from the minimum value 0 to the maximum value 1.

[0060] In this way, the diagnosis unit 13 repeats the process including normalization for each detected current data within each accumulation time TL. The envelope normalized signal Ist (Ist1, Ist2, ...) is obtained for each accumulation time TL (TL1, TL2, ...) in FIG.

[0061] [Process flow (4)-Spectral peak detection process] 3, the diagnostic unit 13 performs a spectrum analysis process using a Fast Fourier Transform (FFT) on the envelope normalized signal Ist for each accumulation time TL to obtain a spectrum that is the result of the spectrum analysis. In the next step S109, the diagnostic unit 13 performs a spectrum peak detection process based on the spectrum of step S108. The diagnostic unit 13 detects a spectrum peak value (P) for each accumulation time TL.

[0062] FIG. 6 shows the spectrum peak detection process in step S109. The graph in FIG. 6 shows a spectrum, with the horizontal axis being frequency [Hz] and the vertical axis being amplitude [dimensionless quantity]. FIG. 6(A) shows an example in which the state of the belt 5 of the power transmission mechanism 6 is normal, and FIG. 6(B) shows an example in which the state is abnormal. In (A), a spectrum peak 601 shows a spectrum peak in a normal state, and is represented by a certain frequency and amplitude value. The frequency of the spectrum peak 601 shows the rotational frequency fc of the rotating machine 4 as the rated frequency. The amplitude value of the spectrum peak 601 is represented by an amplitude value a1. In the envelope signal IhL in the above-mentioned step S106, the rotational frequency fc, which is the rotation information on the rotating machine 4 side, is extracted. As a result, the frequency of the spectrum peak 601 in the normal state in (A) coincides with the frequency band of the rotational frequency fc.

[0063] On the other hand, in (B), a spectrum peak 602 in an abnormal state is represented by a certain frequency and amplitude value. In this example, the frequency of the spectrum peak 602 in an abnormal state is at the same position as the frequency of the spectrum peak 601 in a normal state, i.e., the rotation frequency fc. In the spectrum in an abnormal state, as shown in the figure, a sideband 603 occurs at a different frequency position near the rotation frequency fc of the spectrum peak 602. The amplitude value of the spectrum peak 602 is indicated by amplitude value a2.

[0064] The detection range (corresponding frequency range) of the spectrum peak in step S109 may be a preset range, which may be the possible range of the rotational frequency fc of the rotating machine 4, or may be a part of that range. The range of the rotational frequency fc may be calculated based on the current frequency fm of the electric motor 3 and the pulley ratio (R1:R2) according to Equation 1.

[0065] Here, comparing (A) and (B) of FIG. 6, it can be confirmed that the amplitude value a2 of the spectrum peak 602 in the abnormal state of (B) is smaller than the amplitude value a1 of the spectrum peak 601 in the normal state of (A). This is due to the following action. That is, if there is an abnormality in the transmission of the belt 5, the driving force corresponding to the load on the electric motor 3 side is not transmitted appropriately and efficiently to the rotating machine 4 side. As a result, in the abnormal state, a rotation frequency other than the rotation frequency fc, which is the rated frequency observed in the normal state, appears, and a sideband wave 603 in a frequency band including that rotation frequency is observed. As a result, the amplitude value a2 of the spectrum peak 602 is smaller than the amplitude value a1 of the spectrum peak 601. The amplitude values ​​a1 and a2 are values ​​within the range from 0 to 1.

[0066] In step S109, the diagnostic unit 13 stores the data of the spectrum peak value P (including frequency and amplitude information as shown in FIG. 6) detected for each accumulation time TL in the memory (FIG. 2). Note that, although the example in FIG. 6 shows a case where a peak appears at one frequency, the rotation frequency fc, this is not limiting, and in some actual cases, peaks may appear at multiple frequencies. Even in such a case, the diagnostic process can be applied in the same way.

[0067] [Process flow (5)-Status diagnosis process] Next, in step S110, the diagnosis unit 13 performs arithmetic processing of first information S1 representing the degree of abnormality in belt transmission based on the data of the above spectral peak value P. In this example, the diagnosis unit 13 compares the spectral peak value P (frequency and amplitude) with a set threshold value H (i.e., the spectral peak threshold value), and determines the state value of the first information S1. The threshold value H is a threshold value related to the determination of the first information S1 representing the degree of abnormality in belt transmission, and particularly includes the threshold value of the amplitude. In this example, when the spectral peak value P is less than the threshold value H (P < H), the diagnosis unit 13 sets the value of the first information S1 to 0, and when the spectral peak value P is greater than or equal to the threshold value H (P ≧ H), the diagnosis unit 13 determines the value of the first information S1 to be 1. In the definition of the state in Embodiment 1, the first information S1 takes a value of 0 or 1. S1 = 0 represents a normal state, and S1 = 1 represents an abnormal state. The closer the spectral peak value P is to the threshold value H, the closer the value of the first information S1 is to 0 (normal), representing a state with a small degree of abnormality. The farther the spectral peak value P is from the threshold value H, the closer the value of the first information S1 is to 1 (abnormal), representing a state with a large degree of abnormality.

[0068] (C) in FIG. 6 shows an explanatory diagram regarding the above determination. Let the amplitude value of a certain spectral peak value P be ap, and the threshold value of the amplitude be ah. When the amplitude value ap of the spectral peak value P is greater than or equal to the threshold value ah, the value of the first information S1 is set to 0, and when the amplitude value ap of the spectral peak value P is less than the threshold value ah, the value is set to 1.

[0069] Next, in step S111, the diagnosis unit 13 uses the first information S1 to determine the value of second information S2 related to the diagnosis result and notification, and determines the output control content according to the value. In the definition of the second information S2 in this example, the second information S2 takes a value of 0 or 1. S2 = 0 represents not notifying because it is normal, and S2 = 1 represents notifying a warning regarding an abnormality. In this example, when S1 = 0, the diagnosis unit 13 sets S2 = 0, and when S1 = 1, the diagnosis unit 13 sets S2 = 1.

[0070] In step S112, the diagnosis unit 13 returns to step S102 if S2=0 (N), and proceeds to step S113 if S2=1 (Y). If returning to step S102, the same process is repeated. In step S113, the diagnosis unit 13 transmits a signal corresponding to S1=1 (abnormal) to the display device 14, thereby causing the display screen of the display device 14 to display an image indicating that the power transmission mechanism 6 is in an abnormal state. In addition, the diagnosis unit 13 transmits a signal corresponding to S2=1 to the alarm device 15, thereby causing the alarm device 15 to output an alarm to warn that the power transmission mechanism 6 is in an abnormal state.

[0071] In step S113, the diagnosis unit 13 sets the value of the flag F to 0 in order to perform a diagnosis end process, and the flow ends.

[0072] [Status Judgment] As a method for determining the state of the power transmission mechanism 6 after the normalization process in step S107, in the first embodiment, the spectrum peak value P is compared with a certain threshold value H as described above. This example of the method is not limiting, and various methods can be applied. As another example of the method, a method may be used in which a plurality of threshold values ​​and corresponding plurality of ranges are used to classify the state value of the first information S1 into three or more state values. As another example of the method, a method may be used in which a time threshold is further used to determine that a state such as an abnormal state exists when a state of a certain parameter value continues for a time period equal to or longer than the time threshold.

[0073] [Screen example] FIG. 7 shows an example of a screen displayed on the display device 14, which displays the state of the power transmission mechanism 6 of the rotating machine system 100. Each part 101 including the power transmission mechanism 6 in FIG. 1 is assigned with management identification information (ID). The state value of the first information S1 is displayed for each ID. The diagnostic device 10 displays an image representing the state value so that the user U1 can easily recognize the state of the power transmission mechanism 6. In the screen example of (A), when S1=0, a "normal" character string image is displayed. The diagnostic device 10 continues to display this "normal" state as long as S1=0. In the screen example of (B), when S1=1, an "abnormal" character string image is displayed. When the value of the first information S1 changes from 0 to 1, the diagnostic device 10 switches to displaying this "abnormal" state. Note that the screen is not limited to displaying such states, and may output, for example, a message or the like that prompts the user U1 to take action such as maintenance work.

[0074] FIG. 7C shows a display example in the case where there are multiple parts 101 including a power transmission mechanism 6 to be diagnosed in the rotating machine system 100, and the states of the multiple parts 101 are diagnosed in parallel and simultaneously. Even in this case, this system allows for collective monitoring of the states of the multiple parts 101. In the screen example of FIG. 7C, there are four power transmission mechanisms 6, and images of the first information S1 for each ID are displayed in parallel. For example, only the part 101 of the power transmission mechanism 6 having ID=003 is in an abnormal state with S1=1. The display communication 14 may be a desktop PC, or may be an information terminal such as a tablet or a smartphone. In this case, communication may be performed from the diagnostic device 10 to the information terminals via a communication interface such as Wi-Fi (registered trademark) or Bluetooth (registered trademark).

[0075] [Output control example] Other examples of output control may be as follows. When the diagnostic result is, for example, a value of the first information S1 of 1 (abnormal), the diagnostic device 10 automatically controls the switch 2 in FIG. 1 to switch the switch 2 to the OFF state by giving a control signal to the switch 2. This stops the operation of the rotating machine 4 from the electric motor 3 through the power transmission mechanism 6. The diagnostic device 10 also outputs a message to the effect that the rotating machine 4 has been stopped on the display device 14 or the like, and prompts the user U1 to take action such as maintenance work. Similarly, control of a transition to deceleration operation is also possible.

[0076] [Other screen examples] The diagnostic device 10 may also display graphs such as those shown in Figs. 4, 5, and 6 on the display screen of the display device 14. In particular, an envelope normalized signal Ist as shown in Fig. 5 may be displayed. A spectrum as shown in Fig. 6 may also be displayed together with values ​​of frequency, amplitude, threshold H, and the like. In addition, the screen of the display device 14 allows user setting using the setting unit 17 described above. The user setting allows selection of a judgment method (for example, the method of each embodiment) and setting of a judgment threshold. In addition, the user setting allows timer setting for diagnosis execution and information management of the target rotating machine 4, etc. In addition, the user setting allows setting of the range of load fluctuation of the electric motor 3 and the rotating machine 4.

[0077] [Effects etc. (1)] As described above, according to the diagnostic device 10 and diagnostic method of the first embodiment, even when there is a fluctuation in the load on the electric motor 3 and the rotating machine 4, it is possible to diagnose with high accuracy an abnormality or other condition related to the power transmission mechanism 6, for example, an abnormality in the transmission of the rotational driving force by the belt 5, while suppressing or preventing erroneous diagnosis or the like. In particular, according to the first embodiment, a current sensor is all that is required at the very least, and there is no need to install additional sensors or the like for measuring the load on the electric motor 3 or the rotating machine 4, so that diagnosis can be realized at low cost. According to the first embodiment, it is possible to perform a diagnosis with high accuracy while suppressing erroneous diagnosis or the like, so that it is possible to reduce downtime costs due to regular maintenance, even for devices that operate continuously for long periods of time or have complex mechanisms.

[0078] In the case of the conventional state diagnosis technology for rotating machine systems, a method of diagnosing the state of a power transmission mechanism using a fixed threshold value cannot respond to changes in the load of the rotating machine, and there is a high possibility of erroneous judgments occurring. As a countermeasure against erroneous judgments, a state diagnosis technology that sets a threshold value that varies depending on the load can be considered. However, in this technology, it is necessary to set appropriate threshold values ​​according to each load, and it is also necessary to additionally install a sensor (e.g., a rotation sensor) that measures the load of the rotating machine. In addition, a threshold data table that sets the correspondence between the varying load and the threshold value based on the load information from the sensor, etc., and memory resources for storing the table are also required. In other words, this technology leads to additional costs and a shortage of memory data.

[0079] According to the first embodiment, even if the load of the rotating machine 4 changes based on the detection of the current supplied to the electric motor 3 during the operation of the rotating machine system, the same algorithm can be used to detect the abnormality of the power transmission mechanism 6. In this method, it is basically necessary to detect and process the current to the electric motor 3 as shown in FIG. 1, and it is not necessary to install a special means such as a sensor for measuring the load of the rotating machine 4. In addition, in this system, when the diagnostic device 10 detects an abnormal state, it is possible to notify the user U1 of the occurrence of an abnormality in the belt 5 or the like by immediately issuing a warning. The user U1 may be a worker who performs processing work. In addition, the output of the notification, etc. is not limited to an output near the diagnostic device 10, and may be a notification to a remote predetermined place or destination via communication.

[0080] [Effects etc. (2)] The effects of the first embodiment will be described in more detail with reference to Figs. 8 to 10. As described above, in this system, the rotational frequency fc on the rotating machine 4 side can be extracted from the processing results of the current information on the electric motor 3 side, and the state of the power transmission mechanism 6 can be determined using this information. In particular, in the first embodiment, the rotational frequency fc can be extracted from a spectrum peak that has undergone envelope normalization processing. By using such data, even when there is a load fluctuation, it is possible to perform highly accurate state determination using an algorithm such as a constant threshold value H.

[0081] In the rotating machine 4 of FIG. 1 in which load fluctuations occur, the change in the spectrum peak value P obtained in step S109 will be described for the case of a configuration in which normalization processing is performed in step S107 of FIG. 3 as in the first embodiment, and for a comparative example in which the normalization processing is not performed.

[0082] 8(A) shows an example of load fluctuation in a rotating machine system (part 101 in FIG. 1) in which the load of the rotating machine 4 increases monotonically with time. In the graph of (A), the horizontal axis shows time, and the vertical axis shows the load [dimensionless quantity] of the rotating machine 4. An increase in the load of the rotating machine 4 corresponds to an increase in the rotational work of the electric motor 3.

[0083] 8(B) shows an example of the current detected by the electrical information detection unit 11 when the load on the rotating machine 4 increases as in (A). In the graph of (B), the vertical axis is the current [A]. As in (B), it is known that the load on the rotating machine 4 increases as the rotational work of the electric motor 3 increases, and the peak of the current value increases as the load on the rotating machine 4 increases. An example of the accumulation time TL is also shown in (B) enclosed by a dashed line frame.

[0084] Fig. 9A shows the results of the current in Fig. 8B being repeatedly collected for each accumulation time TL in step S105 and subjected to envelope detection processing in step S106. In the graph in Fig. 9A, the vertical axis is the current [A] of the envelope signal IhL. In this way, when the load fluctuates, the current value after envelope detection increases with the load.

[0085] 9B shows the spectral peak value P when steps S108 and S109 are performed on the current value after envelope detection in (A). The spectral peak value P in the graph in (B) is shown as a transition on the time axis of the tip portion of the spectral peak represented by frequency and amplitude as in FIG. 6. Line 901 shows the transition of the spectral peak value P when the power transmission mechanism 6 is in a normal state. Line 902 shows the transition of the spectral peak value P when it is in an abnormal state. The graph also shows line 903, which represents a constant amplitude threshold as the aforementioned threshold H.

[0086] Comparing line 901 in the normal state with line 902 in the abnormal state, it can be seen that the spectrum peak value P increases with an increase in the load in either state. In this case, for example, in step S110, if the threshold value H for the state value of the first information S1 is set as a constant value independent of the load as shown in line 903, there is a risk of the following. That is, depending on the load, even if the state is actually normal, the spectrum peak value P may be below the threshold value H, for example, at time point 911, and the state may be erroneously determined to be an abnormal state. Also, depending on the load, even if the state is actually abnormal, the spectrum peak value P may exceed the threshold value H, for example, at time point 913, and the state may be erroneously determined to be normal. Also, if the threshold value H is configured to be changed depending on the load, as described above, it is necessary to install a sensor or the like that measures the load, which leads to increased costs, etc.

[0087] Next, with reference to FIG. 10, a case where normalization processing is performed in step S107 in a rotating machine 4 where load fluctuation occurs will be described. Here, the premise that the load in FIG. 8 increases monotonically is the same. FIG. 10(A) shows the result of envelope detection in step S106 of data for each accumulation time TL collected in step S105 for the current in FIG. 8(B) and normalization in step S107. In the graph in (A), the horizontal axis is time and the vertical axis is the current value of the envelope signal IhL. This current value has a value within the range from 0 to 1 due to the normalization effect. In this way, since the current information is normalized to a value within a predetermined range in advance in step S107, the data for each accumulation time TL (current value shown in the figure) always falls within the predetermined range even if the load fluctuates.

[0088] Here, information on the amplitude of the current shown in FIG. 8B (eg, the current amplitude 802) is lost due to the normalization process in step S107.

[0089] 10B shows the spectral peak value P when steps S108 and S109 are performed on the current data in (A). Line 1001 shows an example of the transition of the spectral peak value P in a normal state. Line 1002 shows an example of the transition of the spectral peak value P in an abnormal state. Line 1003 shows an example of a constant value (amplitude threshold) as the threshold H.

[0090] Here, as shown in the figure, the amplitude of the spectrum peak value P converges within the range from 0 to 1 under both normal and abnormal conditions. Unlike FIG. 9B, both lines 1001 and 1002 converge to a constant value. It can be seen that the line 1001 in the normal state is a higher value than the line 1002 in the abnormal state because the power generated by the electric motor 3 is efficiently transmitted through the power transmission mechanism 6. Furthermore, because normalization processing has been performed in step S107, the spectrum peak does not increase as shown in the figure even when the load increases.

[0091] Therefore, in the diagnostic device 10 and diagnostic method of the first embodiment, even when there is a load fluctuation in the rotating machine 4, it is possible to diagnose the state of the power transmission mechanism 6 by the diagnostic process including the normalization process in step S107 in Fig. 3. That is, as shown in Fig. 10, it is possible to diagnose the state of the power transmission mechanism 6 by comparing the spectrum peak value P, which represents the above-mentioned rotational frequency fc, with a certain threshold value H.

[0092] In the first embodiment, the data and timing to be normalized are the data and timing after the envelope detection process in step S106, and data processing is performed every predetermined time (accumulation time TL). This also has the effect of reducing the amount of processed data and the processing time.

[0093] <Embodiment 2> A diagnostic device and a diagnostic method according to a second embodiment of the present invention will be described with reference to Figs. 11 to 13. The basic configuration of the second embodiment is the same as that of the first embodiment, and the following describes the components of the second embodiment that are different from the first embodiment. In the second embodiment, learning is used for the diagnostic process.

[0094] [Diagnosis section - Processing flow] Fig. 11 shows a flow of diagnostic processing by diagnostic section 13 in diagnostic device 10 of embodiment 2. Fig. 11 has steps S201 to S216. Steps S203 to S209 and S210 to S214 in the flow in Fig. 11 are similar to steps S103 to S109 and S110 to S114 in the flow in Fig. 3, and steps S201, S202, S215, and S216 are different.

[0095] In step S201, diagnosis unit 13 sets flag F. In the second embodiment, flag F has a value of 0 indicating stop, a value of 1 indicating start, and a value of 2 indicating learning. In the second embodiment, learning is possible in diagnosis device 10, and F=2 is the value when learning is performed (referred to as "learning mode"). In step S202, if flag F has a value of 1 or 2, the process proceeds to step S203, and if flag F has a value of 0, the flow ends. In steps S203 to S209, diagnosis unit 13 performs the same process as in the first embodiment, and obtains spectrum peak value P as a result of step S209.

[0096] Next, in step S215, the diagnosis unit 13 checks whether the value of the flag F is 2, that is, whether the learning mode is selected. If F=2, the process proceeds to step S216, and if F=1, the process proceeds to step S210.

[0097] In step S216, the diagnosis unit 13 performs a normal data learning process. The diagnosis unit 13 learns the spectral peak value P, which is the calculation result in step S209 when the power transmission mechanism 6 is in a normal state, as normal data. Data related to the learning is stored in memory. After step S216, the flow ends and similarly repeats. In step S216, in the example of the second embodiment, the diagnosis unit 13 saves the arithmetic mean value of the spectral peak values ​​P in memory as the spectral peak Ph of normal data. This spectral peak Ph of normal data is calculated by the following equation 7. Σ is the sum of the spectral peak values ​​P from time t=0 to n. Ph is the value obtained by dividing this sum by n. Equation 7: Ph={ΣP(t)} / n

[0098] If learning has been completed, the learning data is referenced in step S210. In step S216, the diagnosis unit 13 references the spectrum peak Ph of the learned normal data and the spectrum peak value P obtained in step S209. The diagnosis unit 13 calculates a state value (referred to as S1(t)) of the first information S1 indicating the degree of transmission abnormality of the belt 5 from the spectrum peak Ph of the normal data and the spectrum peak value P. In this case, the state value S1(t) is a value for each time point t, and is calculated by the following formula 8. P(t) is the spectrum peak value P for each time point t. Equation 8: S1(t)=1-P(t) / Ph

[0099] Next, in step S213, the diagnosis unit 13 compares the state value S1(t) of the first information S1 with a preset threshold H, and if the state value S1(t) is less than the threshold H, it determines that the state is normal and sets the value of the second information S2 to 0, whereas if the state value S1(t) is equal to or greater than the threshold H, it determines that the state is abnormal and sets the value of the second information S2 to 1. In step S214, if S2=1, the diagnosis unit 13 issues a warning notifying of the abnormal state in step S213. In step S214, the flag F is set to 0, and the flow ends.

[0100] [Example] FIG. 12 shows an output example on the screen of the display device 14 in Embodiment 2. (A) shows an example of the screen when the current state is normal (S1 < H). In this screen, an image representing the "normal" state at the current time and a graph related to the time-series transition of the state up to the current time are displayed. In the graph, the horizontal axis represents time (t), the vertical axis represents the state value of the first information S1 representing the degree of transmission abnormality, and a line representing the threshold value H is also displayed. (B) shows an example of the screen when the current state is abnormal (S1 ≧ H). In this screen, an image representing the "abnormal" state at the current time and a similar graph are displayed. By looking at a screen like FIG. 12, the user U1 can confirm the transition of the state value (degree of abnormality) of the first information S1. The diagnostic device 10 updates the screen display content in real time according to the change of the state. The same display is applicable also in the case of batch monitoring of a plurality of rotating machines 4 and the like.

[0101] [Effects, etc.] As described above, according to Embodiment 2, in addition to the same effects as in Embodiment 1, it is possible to determine an abnormal state based on the learning of normal state data, and the degree of abnormality (state value S1) of the power transmission mechanism 6 can be quantitatively presented to the user U1 in time series. Thereby, maintenance inspection work such as replacement of deteriorated parts can be made easier.

[0102] <Embodiment 3> Embodiment 3 of the present invention will be described with reference to FIG. 13. Embodiment 3 is a modification of Embodiment 2. In Embodiment 2, in step S210 of FIG. 11, the state value of the first information S1 was calculated from the spectral peak value P and the additive average value Ph of the spectral peak value P. Regarding the calculation of the state value of this first information S1, in Embodiment 3, as follows, the Mahalanobis distance is calculated and this Mahalanobis distance is used as the state value.

[0103] [Mahalanobis distance] In order to calculate the Mahalanobis distance, the diagnosis unit 13 calculates the average value, standard deviation, and correlation matrix within the normal data in the above-mentioned step S216 to create a Mahalanobis space. When the spectrum peak value P of the normal data and the state quantity related to the spectrum peak value P are given by matrix X, it is shown in the following formula 9. From the matrix X of formula 9, the Mahalanobis distance is calculated as in formulas 10 and 11. Let the Mahalanobis distance be represented by D. Xm represents the average value of the state quantity. σ represents the standard deviation of the state quantity. R represents the correlation matrix. Xm T represents the transpose of Xm. Equation 9: X=[X 1 ,X 2 ,……,X n ] Equation 10: Xm=[(X 1 -X m1 ) / σ 1 ,(X 2 -X m2 ) / σ 1 ,……,(X n -X mn ) / σ n ] Equation 11: D=√{1 / n×XmR -1 xXm T} (D≧0)

[0104] The process flow of the diagnostic device 10 in the third embodiment is the same as that in Fig. 11. The Mahalanobis distance D in the case where normal data has been learned in step S216 for a certain period of time (when flag F=0 is set after a certain period of time has elapsed since flag F=2 was input in step S201) will be described below.

[0105] Fig. 13 is an explanatory diagram of the Mahalanobis distance D. Fig. 13(A) shows time series data of the spectral peak value P from the time ta when flag F=2 is input to the time tb when F=0 (i.e., the learning time). In this graph, the horizontal axis is time (t) and the vertical axis is the spectral peak value P. The upper point group shows normal data 1301 in a normal state, and the lower point group shows abnormal data 1302 in an abnormal state. The abnormal data 1302 has a reduced spectral peak value P compared to the normal data 1301. Here, the Mahalanobis distance D is calculated for data 1303 shown by black dots in (A).

[0106] FIG. 13B shows the probability density function of normal data 1311 and abnormal data 1312 with respect to the value (referred to as "normalized distance") obtained by dividing the spectrum peak value P by the standard deviation σ. In this graph, the horizontal axis is the normalized distance (P / σ) [dimensionless quantity], and the vertical axis is the probability density [dimensionless quantity]. The normal data 1311 corresponds to the normal data 1301, and the abnormal data 1312 corresponds to the abnormal data 1302. The average 1313 is the average of the normal data 1311. Based on the above-mentioned formulas 10 and 11, the Mahalanobis distance D can be defined as the normalized distance (P / σ) from the average 1313 of the normal data 1311 to the target data 1303, as shown in the figure. The Mahalanobis distance D for each data can be used as a state value representing the degree of transmission abnormality. In other words, the larger the Mahalanobis distance D, the greater the degree of abnormality.

[0107] [Effects, etc.] According to the third embodiment, in addition to the same effects as those of the second embodiment, the following effects are obtained. In the case of a rotating machine 4 with a load fluctuation, for example, a rotating machine 4 with large data variance of the spectrum peak value P, when there is still a small amount of learning data, the variation in the state value of the first information S1 also becomes large. By increasing the amount of learning data, the standardized distance and the Mahalanobis distance D are updated. This makes it easier to distinguish between normal data and abnormal data, as in the example of FIG. 13, i.e., the accuracy of diagnosis is improved.

[0108] 13, a one-dimensional Mahalanobis distance using one variable, the spectrum peak value P, particularly the arithmetic mean value Ph, has been described. However, if various state quantities can be acquired by a sensor or the like, a multidimensional Mahalanobis distance using a plurality of these variables can be calculated in the same manner and used as the first information S1.

[0109] <Fourth embodiment> A fourth embodiment of the present invention will be described with reference to Fig. 14. The fourth embodiment is a modified example of the first embodiment. Fig. 14 shows the configuration of a rotating machine system 100 including a diagnostic device 10 of the fourth embodiment. In the fourth embodiment, the above-mentioned diagnostic device 10 is implemented so as to be built into the switch 2 and the electric motor 3 as an integral part. In the fourth embodiment, in particular, the diagnostic device 10 is implemented as a part of a controller 31 of the electric motor 3.

[0110] In FIG. 14, the electric motor 3 has a switch unit 32, a controller 31, and a motor unit 33. The function of the switch unit 32 is the same as that of the switch 2 described above. The switch unit 32 and the controller 31 may be integrated. The motor unit 33 is a mechanical structure such as a rotor and a stator that constitute a three-phase induction motor, in other words, a drive unit. The controller 31 includes a drive control unit 34 that is a part that controls the drive of the motor unit 33, and can be composed of, for example, a power conversion device such as an inverter, an IC board, etc. The controller 31 is equipped with the electrical information acquisition unit 11 and the diagnosis unit 13 described above. A configuration in which the electrical information acquisition unit 11 and the diagnosis unit 13 are equipped in the switch unit 32 is also possible.

[0111] As a result of the diagnostic process by the diagnosing unit 13, the above-mentioned first information S1 and second information S2 are output. These pieces of output information are output from the controller 31 to the external display device 14 and / or the notification device 15 via communication. The diagnosing unit 13 may also output an instruction for output control, such as stopping operation or decelerating operation, according to the first information S1 and / or the second information S2 to the drive control unit 34. The drive control unit 34 drives and controls the motor unit 33 according to the instruction. The user U1 uses the functions of the diagnosing device 10 by operating the operation unit 16 or the like external to the controller 31.

[0112] As described above, according to the fourth embodiment, it is possible to diagnose the condition of the power transmission mechanism 6 using the diagnostic device 10 integrally implemented in the electric motor 3, and it is also possible to perform drive control to deal with abnormalities, etc., depending on the results of the condition diagnosis.

[0113] <Fifth Preferred Embodiment> A fifth embodiment of the present invention will be described with reference to Fig. 15. The fifth embodiment is a modified example of the first and fourth embodiments. Fig. 15 shows a configuration of a rotating machine system 100 including a diagnostic device 10 of the fifth embodiment. In the fifth embodiment, the diagnostic device 10 described above is implemented by a communication device 50 and a diagnostic server 500. The communication device 50 is connected to the part 101. The diagnostic server 500 is located remotely from the communication device 50 via a communication network 501. The communication network 501 is a wide area network, a LAN, or the like.

[0114] The communication device 50 is disposed, for example, in close proximity to the part 101 including the electric motor 3 and the power transmission mechanism 6. The communication device 50 has an electric information detection unit 51, an electric information storage unit 52, and a communication unit 53. The electric information detection unit 51 is connected to the power line 1L through a detection electric wire 1S, for example, similar to the first embodiment. The electric information detection unit 51 detects a current, similar to the electric information detection unit 11 of the first embodiment. The electric information detection unit 51 also includes an ADC that converts an analog current signal into a digital signal, etc.

[0115] The electrical information storage unit 52 stores and holds data on the detected current, which is the electrical information obtained by the electrical information detection unit 51, in a memory configured of a non-volatile memory or the like. The communication unit 53 includes a communication interface device, and transmits the detected current data stored in the electrical information storage unit 52 to the diagnostic server 500 via the communication network 501. Wi-Fi (registered trademark), Bluetooth (registered trademark), or the like can be used as the communication interface. This data communication may be automatically performed at a preset timing, or may be performed in response to a request from the diagnostic server 500.

[0116] The diagnostic server 500 includes a communication unit 54, an electrical information acquisition unit 55, a database (DB) 56, and a diagnostic unit 13. The communication unit 54 includes a communication interface device and performs communication processing with the communication device 50 via a communication network 501. The communication unit 54 acquires detected current data from the communication device 50 and stores it in a memory or DB 56. The diagnostic unit 13 performs diagnostic processing using the acquired data in the same manner as in the first embodiment, etc., stores the result in the DB 56, and causes the display device 14 or the notification device 15 to output it. The user U1 can recognize the state of the power transmission mechanism 6 of the rotating machine system 100 by using the diagnostic server 500 based on an operation from the operation unit 16 or his / her own PC, etc.

[0117] Similarly, the diagnostic server 500 may collect data from multiple parts 101, not limited to the part 101 including one rotating machine 4, diagnose the data, and store the data in the DB 56. The diagnostic server 500 may perform analysis processing, etc., using the information in the DB 56. The user U1 can appropriately display and check the information in the DB 56 on the screen of the display device 14.

[0118] As described above, according to the fifth embodiment, the electrical information can be temporarily stored in the communication device 50, and the condition can be diagnosed by the remote diagnostic server 500. Since the electrical information can be stored in the communication device 50, even if the communication speed of the communication network 501 is slow, for example, it is not necessary to reduce the detection frequency of the electrical information detection unit 51. Furthermore, since the communication device 50 including the electrical information detection unit 51 and the diagnostic server 500 including the diagnostic unit 13 can be completely separated, for example, a user U1 such as a maintenance inspector can remotely diagnose the power transmission mechanism 6, and it is possible to reduce the risk of entanglement accidents during inspection and the number of inspection steps.

[0119] <Sixth embodiment> A sixth embodiment of the present invention will be described with reference to Figs. 16 and 17. The first embodiment and others have described a method for diagnosing the state of the power transmission mechanism 6 based on the relationship between the current frequency fm of the electric motor 3 and the rotational frequency fc of the rotating machine 4, in particular a state diagnosis method that can handle load fluctuations by using normalization processing. In contrast, the sixth embodiment describes a state diagnosis method that can handle load fluctuations without using normalization processing, based on the relationship between the current frequency fm of the electric motor 3 and the rotational frequency fc of the rotating machine 4. In the sixth embodiment, the rotational frequency fc, which is rotation information on the rotating machine 4 side, is obtained as an estimated rotational frequency by an estimation calculation.

[0120] FIG. 16 shows the configuration of a rotating machine system 100 including a diagnostic device 10 of the sixth embodiment. The diagnostic device 10 has a rotation information calculation unit 12 in addition to the components in FIG. 1. The rotation information calculation unit 12 inputs a current signal E1 (or digitized detected current data) which is electrical information from the electrical information detection unit 11, and calculates rotation information on the rotating machine 4 side. The rotation information calculation unit 12 outputs processing result information including the rotation information to a diagnostic unit 13. The diagnostic unit 13 uses the information to perform diagnostic processing the details of which differ from those of the first embodiment (FIG. 3).

[0121] The diagnostic device 10 of the sixth embodiment obtains an estimated rotation speed (Efm) of the electric motor 3 and an estimated rotation frequency (Efc) of the rotating machine 4 by performing estimation calculations. The estimated rotation speed Efm is estimated information corresponding to the current frequency fm. The estimated rotation frequency Efc is estimated information corresponding to the rotation frequency fc. Then, the diagnostic unit 13 diagnoses the state of the power transmission mechanism 6 based on the relationship between the estimated rotation speed Efm and the estimated rotation speed Efm.

[0122] As described above, when the transmission of the belt 5 of the power transmission mechanism 6 is in a normal state, the relationship between the current frequency fm of the motor 3 and the rotation frequency fc of the rotating machine 4 is expressed by Equation 1. The current frequency fm of the motor 3 is also a rotation frequency corresponding to the rotation speed of the motor 3, and can be known in advance by the frequency of the AC power supplied to the motor 3 or a sensor. In the first embodiment, the diagnostic device 10 detects a peak of a frequency (rotation frequency fc on the rotating machine 4 side) different from the peak of the current frequency fm of the motor 3, and monitors or observes the change in the magnitude of the peak to determine the state. The spectrum peak value P in the above-mentioned FIG. 6 represents the rotation frequency fc of the rotating machine 4. This makes it possible to diagnose and detect the presence of some abnormality or symptom in the belt 5, pulleys (3P, 4P), the motor 3, or the rotating equipment 4, which are representative components of the rotating machine system.

[0123] However, when the peak of the current frequency fm increases or decreases due to a change in the load of the rotating machine 4 (FIGS. 8 and 9), the peak of a frequency (assumed to be fx) different from the current frequency fm often increases or decreases following the peak of the current frequency fm. In the case of a technology that monitors only the peak of the different frequency fx in such a state where frequencies follow each other, the peak of the different frequency fx increases, so that even if the rotating machine system is operating normally, it may be erroneously determined as an abnormal state or a symptom of an abnormal state.

[0124] Furthermore, in the case of a technology that monitors only the magnitude of the peak of the different frequency fx, depending on the load connected to the rotating machine 4, the peak of the current frequency fm may decrease, but the peak of the different frequency fx may not change. This is the case when no abnormality occurs in the belt 5 or the pulleys (3P, 4P) and the rotational driving force is properly transmitted from the electric motor 3 to the rotating machine 4. In this case, even though the rotating machine system is operating normally, the peak of the different frequency fx increases, so it may be erroneously determined as an abnormality or symptom state.

[0125] Therefore, in the diagnosis device 10 of the sixth embodiment, a state such as an abnormality of the part 101 is judged and detected by a processing method that does not use the envelope detection of step S106 and the normalization process of step S107 in Fig. 3 for the current signal E1, which is the electrical information detected by the electrical information detection unit 11. As a result, in the sixth embodiment, the problem can be solved by a different processing method while still using the relationship between the current frequency fm of the electric motor 3 and the rotational frequency fc of the rotating machine 4 as in the first embodiment.

[0126] In Fig. 16, the rotation information calculation unit 12 can be realized by software program processing or a hardware circuit, etc. The rotation information calculation unit 12 obtains electrical information by sampling the current signal E1, which is electrical information measured from the power line 1L by the electrical information detection unit 11, at a predetermined time. The rotation information calculation unit 12 calculates an estimated rotation speed Efm as a calculation to specify the current frequency fm of the electric motor 3 from detected current data, which is information in which the electrical information is arranged in chronological order, as shown in a balloon in Fig. 16.

[0127] Next, the rotation information calculation unit 12 compares the peak of the current frequency fm (corresponding estimated rotation speed Efm) with the peak of a frequency fx different from the current frequency fm. The rotation information calculation unit 12 calculates an estimated rotation frequency Efc as information corresponding to the above-mentioned rotation frequency fc as the different frequency fx.

[0128] That is, in the method of the sixth embodiment, the peak of the current frequency fm (corresponding estimated rotation speed Efm) and the peak of the different frequency fx (corresponding estimated rotation frequency Efc) are compared in relative value. Therefore, the information corresponding to the current frequency fm can be used as a reference to compare the information corresponding to the different frequency fx.

[0129] The peak of the current frequency fm and the peak of the different frequency fx used in this comparison are not limited to the above-mentioned spectrum peak value P, but may be values ​​obtained by smoothing the frequencies around these frequencies (fm, fx).

[0130] As a result, in the sixth embodiment, unlike the technique of monitoring only the different frequencies fx (for example, the rotation frequency fc), even if the peak of the current frequency fm changes depending on the magnitude of the load as described above, the peak of the different frequencies fx is compared based on the magnitude of the peak of the current frequency fm. Therefore, it is possible to detect a state such as an abnormality or a symptom thereof in response to the load fluctuation of the rotating machine 4.

[0131] 17, as a supplement to the sixth embodiment, shows examples of the peak of the current frequency fm and the peak of the different frequency fx according to the magnitude of the load. (A) is an example when the load is small, and a spectral peak 1701 has a current frequency fm and an amplitude value a11. A spectral peak 1702 has a frequency fx different from the current frequency fm that follows the current frequency fm and an amplitude value a12. (B) is an example when the load is large, and a spectral peak 1703 has a current frequency fm and an amplitude value a13 (a13>a11). A spectral peak 1704 has a frequency fx different from the current frequency fm that follows the current frequency fm and an amplitude value a14 (a14>a12). In (A), a relative value 1711 is the difference in amplitude between the spectral peak 1701 and the spectral peak 1702. In (B), a relative value 1712 is the difference in amplitude between the spectral peak 1703 and the spectral peak 1704. The diagnostic device 10 uses the relative value of such peak-to-peak comparisons to determine the condition of the driveline 6, for example by comparison with a threshold value.

[0132] [Effects, etc.] As described above, according to the sixth embodiment, the same effects as those of the first embodiment etc. can be obtained. Moreover, according to the sixth embodiment, since the envelope detection process is not used, the amount of calculation can be reduced accordingly, and it becomes easy to implement the sixth embodiment in, for example, a microcomputer used in an embedded device.

[0133] In addition, in embodiment 6, in order to further improve the diagnostic accuracy, it is also possible to perform FFT processing on the acquired detected current data and then compare the peak of the current frequency fm with the peak of the different frequency fx.

[0134] Furthermore, in the sixth embodiment, a statistical process such as moving average or weighted average may be performed on the peak of the current frequency fm and the peak of the different frequency fx. In this case, there is an effect that the accuracy of detecting a state such as an abnormality is improved by corresponding to a time delay with respect to a change in the load of the rotating machine 4. For example, the diagnostic device 10 calculates an average of a sampling value of the latest peak of the current frequency fm and a sampling value of a peak of the current frequency fm before the previous time. The diagnostic device 10 compares the average with a sampling value of the latest peak of the different frequency fx. Alternatively, the diagnostic device 10 may compare the average with an average of a peak of the latest peak of the different frequency fx and a peak of the different frequency fx before the previous time. These configurations are effective when an instantaneous shock is applied to the rotating machine system, when electrical noise occurs around the AD conversion or in the power line 1L, etc.

[0135] An example of the envelope processing used in the sixth embodiment is as follows. The diagnostic device 10 performs envelope processing on the detection current data obtained by plotting the current measured by the electrical information detection unit 11 in a time series. This envelope processing makes it possible to draw a curve connecting each peak in a composite wave having multiple frequency bands. Then, in the waveform after this envelope processing, it becomes possible to observe not only the sideband waves themselves generated by each frequency or signal of the composite wave, but also the overall picture of the electrical information. The method of the sixth embodiment can be similarly applied to the learning mode function of the second embodiment.

[0136] [Variations] The above-mentioned sixth embodiment is a configuration example in which the envelope detection process is not performed. However, as a modification of the sixth embodiment, a configuration example in which the envelope detection process is used will be described. As shown in the envelope signal IhL in (A) of FIG. 5 described above, in the waveform in which the process of detecting or acquiring the envelope has been performed, the part that repeatedly becomes convex with respect to time corresponds to the rotation frequency fc. The diagnostic device 10 can acquire the peak of the rotation frequency fc by measuring the peak in a predetermined section taking the rotation frequency fc into consideration by a process such as moving average. Meanwhile, the frequency fx different from the rotation frequency fc in the predetermined section is set to the waveform size other than the convex part of the rotation frequency fc. The waveform of this different frequency fx can be obtained by a process of integrating the waveform other than the convex part or a process of moving average of the size of a predetermined timing.

[0137] The diagnostic device 10 can obtain information on the waveform of a frequency fx different from the waveform of the rotational frequency fc by the above-mentioned processing. Then, the diagnostic device 10 compares the waveform of the different frequency fx with the waveform of the rotational frequency fc as a reference. That is, the diagnostic device 10 checks the relative value between the waveform of the rotational frequency fc and the waveform of the different frequency fx. As a result, even in this modified example, it is possible to detect conditions such as abnormalities and symptoms in response to load fluctuations in the rotating machine 4.

[0138] Although the present invention has been specifically described above based on the embodiments, the present invention is not limited to the above-mentioned embodiments and can be modified in various ways without departing from the scope of the present invention. A combination of the embodiments is also possible. The technology disclosed herein is widely applicable to rotating machine systems including a power transmission mechanism. In each of the above-mentioned embodiments, the diagnostic device 10 obtains rotation information as information corresponding to the rotation frequency fc on the rotating machine 4 side in relation to the current frequency fm on the motor 3 side based on detection and measurement of current from the range from the AC power source 1 to the motor 3. Then, by using this information, the diagnostic device 10 can diagnose the state of the power transmission mechanism 6 with a predetermined algorithm even when there is a load fluctuation on the rotating machine 4. When obtaining the above-mentioned rotation information, as described above, normalization processing, envelope detection processing, estimation calculation, frequency change (different frequency fx), etc. can be used. Not limited to this, any processing for obtaining the above-mentioned rotation information can be applied. For example, a method of counting the occurrence time of peak intervals in an envelope as shown in FIG. 5 and calculating an estimated rotation frequency Efc of the rotating machine 4 from the occurrence time of the peak interval can be applied. [Explanation of symbols]

[0139] 1...AC power supply, 2...switch, 3...electric motor, 4...rotating machine, 3P...first pulley, 4P...second pulley, 5...belt, 6...power transmission mechanism, 10...diagnostic device, 11...electrical information detection unit, 13...diagnostic unit, 14...display unit, 15...alarm device, 100...rotating machine system, 101...part.

Claims

1. an electric motor driven by power supplied from a power source; a power transmission mechanism connected to the electric motor; a rotating machine connected to the power transmission mechanism; and a diagnostic device connected to the electric motor or to a power supply line from the power source to the electric motor, for diagnosing a state of the power transmission mechanism; The diagnostic device in a rotating machine system comprising: the power transmission mechanism includes a first pulley that outputs a rotational driving force of the electric motor, a second pulley that drives the rotating machine, and a drive transmission component that connects the first pulley and the second pulley, When a load applied to the rotating machine from the electric motor through the power transmission mechanism fluctuates, The diagnostic device comprises: Detecting a current in the power supply; calculating an estimated rotation speed of the motor based on the detected current value in a time series; Calculating an estimated rotational frequency of the rotating machine based on the estimated rotational speed of the electric motor; extracting a peak of a current frequency corresponding to an estimated rotation speed of the electric motor and a peak of a frequency different from the current frequency corresponding to an estimated rotation frequency of the rotating machine; a state of the power transmission mechanism is determined using a relative value obtained by comparing a peak of the current frequency with a peak of the frequency different from the current frequency based on a relationship between a current frequency corresponding to the estimated rotation speed of the electric motor and a frequency different from the current frequency corresponding to the estimated rotation frequency of the rotating machine, and the state of the power transmission mechanism is determined and a diagnosis result is stored and output. Diagnostic equipment for rotating machinery systems.

2. 2. The diagnostic device for a rotating machine system according to claim 1, the diagnostic device calculates a spectral peak based on a spectral analysis for a frequency band including an estimated rotation frequency of the rotating machine, and determines a state of the power transmission mechanism using a frequency and an amplitude of the spectral peak. Diagnostic equipment for rotating machinery systems.

3. 3. The diagnostic device for a rotating machine system according to claim 2, The diagnostic device comprises: With learning mode, In the learning mode, learning is performed using the spectrum peak when the power transmission mechanism is in a normal state as normal data, and data related to the learning including the normal data is stored in a memory; After the learning is completed, when a diagnosis is performed other than in the learning mode, a state of the power transmission mechanism is determined based on the spectrum peaks in the normal data and the spectrum peaks calculated based on the spectrum analysis of the diagnosis target, using the data related to the learning. Diagnostic equipment for rotating machinery systems.

4. 4. The diagnostic device for a rotating machine system according to claim 3, the diagnostic device determines a state of the power transmission mechanism based on a ratio between the spectrum peak in the normal data serving as a reference and the spectrum peak in the diagnostic target. Diagnostic equipment for rotating machinery systems.

5. 3. The diagnostic device for a rotating machine system according to claim 2, the diagnostic device calculates a Mahalanobis distance using time series data of the spectrum peaks, and judges a state of the power transmission mechanism using the Mahalanobis distance. Diagnostic equipment for rotating machinery systems.

6. 3. The diagnostic device for a rotating machine system according to claim 2, The diagnostic device comprises: a communication device connected to the electric motor; a server device connected to the communication device via a communication network; having The communication device stores data of the detected current and transmits the data to the server device; the server device determines a state of the power transmission mechanism by using the data acquired from the communication device; Diagnostic equipment for rotating machinery systems.

7. an electric motor driven by power supplied from a power source; a power transmission mechanism connected to the electric motor; a rotating machine connected to the power transmission mechanism; and a diagnostic device connected to the electric motor or to a power supply line from the power source to the electric motor, for diagnosing a state of the power transmission mechanism; A diagnostic method for a rotating machine system comprising: the power transmission mechanism includes a first pulley that outputs a rotational driving force of the electric motor, a second pulley that drives the rotating machine, and a drive transmission component that connects the first pulley and the second pulley, When a load applied to the rotating machine from the electric motor through the power transmission mechanism fluctuates, The steps executed by the diagnostic device include: detecting a current in the power supply; calculating an estimated rotation speed of the motor based on the detected current values ​​on a time series basis; calculating an estimated rotational frequency of the rotating machine based on the estimated rotational speed of the electric motor; extracting a peak of a current frequency corresponding to an estimated rotational speed of the electric motor and a peak of a frequency different from the current frequency corresponding to an estimated rotational frequency of the rotating machine; a step of determining a state of the power transmission mechanism using a relative value obtained by comparing a peak of the current frequency with a peak of a frequency different from the current frequency based on a relationship between a current frequency corresponding to an estimated rotational speed of the electric motor and a frequency different from the current frequency corresponding to an estimated rotational frequency of the rotating machine, and storing and outputting a diagnosis result; A method for diagnosing a rotating machine system comprising:

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