Low-Cost Microcontroller-Based System for Condition Monitoring of Permanent-Magnet Synchronous Motor Stator Windings

Abstract

Permanent-magnet synchronous motors (PMSMs) have played a key role in recent years in both industrial and commercial applications. Despite their many significant advantages, such as high efficiency, very good dynamics, and high power density, these types of motors are prone to various types of faults. This article proposes a low-cost microcontroller-based system for PMSM stator winding condition monitoring and fault diagnosis. It meets the demand created by the use of more and more low-budget solutions in industrial and commercial applications. A printed circuit board (PCB) has been developed to measure PMSM stator phase currents, which are used as diagnostic signals. The key components of this PCB are LEM’s LESR 6-NP current transducers. The acquisition and processing of diagnostic signals using a low-cost embedded system (NUCLEO-H7A3ZI-Q) with an ARM Cortex-M core is described in detail. A machine learning-driven KNN-based fault diagnostic algorithm is implemented to detect and classify incipient PMSM stator winding faults (interturn short-circuits). The effects of the severity of the fault and the motor operating conditions on the symptom extraction process are also investigated. The results of experimental tests conducted on a 2.5 kW PMSM confirmed the effectiveness of the developed system.

1. Introduction

AC motors have for many years been key components of drive systems used in a wide variety of industries, including the automotive, aviation, robotics, and automation fields. They are also increasingly used in commercial and residential markets, such as in household appliances, as well as in Heating, Ventilation, and Air Conditioning (HVAC) systems. AC motors are an integral part of both global industry and everyday life. According to a report by the International Energy Agency (IEA), systems powered by electric motors account for about 70% of the electricity consumed by global industry [1,2]. For commercial buildings, this is estimated to be as much as 38% of total energy required [3].

Among the most popular types of AC motors in use today are induction motors (IMs) and permanent-magnet synchronous motors (PMSMs). IMs are characterized by high reliability, relatively high efficiency, and low manufacturing and operating costs. They account for a significant proportion of all electric motors installed in industrial scenarios. Nevertheless, they are gradually being replaced by PMSMs, which have significantly gained popularity in recent years due to their characteristics, such as very high efficiency, high power density, excellent dynamics, and wide speed-control range. This popularity increase is also linked to global trends of electrification, sustainability, and eco-design, within which high-efficiency AC motors fit perfectly. In addition, PMSMs have a much higher power density compared to IMs. Thus, they are more compact, reducing the amounts of raw materials required to produce a motor of a given power [4].

Despite the use of increasingly optimal materials for producing electric motors in terms of quality, reliability, and durability, these motors can still suffer various types of faults during operation. These faults can limit or disable their further operation, thus causing downtime in the industrial process or failure of the equipment in which they are installed. Early fault-detection may enable proper motor overhaul planning, resulting in reduced repair costs, shorter potential delays, and reduced economic losses. Given the above and the aforementioned popularity of PMSMs, diagnostics of their faults are of great importance to university researchers and industry [5].

Ensuring safety, continuity of operation, and reliability becomes possible through the use of real-time condition monitoring and fault diagnosis systems for individual motor components. Implementing an effective diagnostic system avoids unplanned downtime and, if the fault is detected at an early stage of its propagation, makes it possible to repair the machine. This is extremely important, not only in terms of economics, but also given the concern for the environment and sustainability, as it reduces the generation of additional waste. Modern diagnostic systems are in line with the ideas and challenges associated with the rapidly developing concept of Industry 4.0, which is directed at automating both the operation of machines and the monitoring of their condition. To meet these requirements, the development of such systems forces the use of the latest technologies and innovative solutions.

In PMSM drive systems, faults may occur not only in the motor itself but also in other drive components, such as the voltage source inverter (VSI) [6] and the measurement equipment. In the case of the VSI, the fault mainly affects the power semiconductor devices, especially the semiconductor switches [7] and the DC-Link capacitors [8]. Defects in measurement equipment are mainly related to faults in the measuring sensors, such as resolvers, encoders [9,10] and current and voltage sensors [11,12]. Given the purpose of this article, special attention will be paid to faults related to the motor.

According to a report published by the Electrical Power Research Institute (EPRI), in which 7500 AC motors of various power were considered, the most common faults include bearing (41%), stator winding (37%) and rotor (10%) faults [13]. In turn, according to a study commissioned by the European Insurance Company (EIC) on high-voltage motors, it appears that as the power rating of electric motors increases, the percentage of bearing faults decreases (13%) in favor of stator winding faults, which are predominant by far (66%) [14]. Electrical faults primarily include damage to the stator windings. They are the result of partial or complete damage to the winding insulation. This can lead to short-circuits in the stator winding. Depending on the type of the short-circuit, the following distinctions can be made [15]:

• Interturn short-circuits (ITSC);
• Short-circuits between the coils in one phase;
• Phase-to-ground short-circuits;
• Phase-to-phase short-circuits.

Stator winding faults also include open-circuit (open-phase) faults [16]. A three-phase PMSM with an open-circuit fault in one of the phases is still capable of operating within a certain load range. However, this type of fault introduces a significant asymmetry in currents and voltages and thus causes the motor to malfunction. Compared to ITSCs, an open-circuit fault occurs less frequently and is most often caused by the cracking of the solders connecting the windings under vibration or the improper connection of the motor. Typical types of stator winding faults are presented in Figure 1. Short-circuits in the stator winding are not only one of the most common AC motor faults, but also the most destructive. Typically, this type of fault starts with ITSC (short-circuit between adjacent turns in one of the coils), which causes current to flow in the shorted circuit with an amplitude usually reaching multiples of the rated current. The short-circuit current causes a significant local increase in the temperature of the winding, leading to further degradation of the insulation.

Since, in the initial stage of the stator winding fault (the ITSC stage), the motor can continue to run, the effects of the short-circuit may spread to the entire winding, leading to a phase-to-phase or phase-to-ground fault in a short time. This is associated with permanent damage to the motor and the need to take it out of service. Another consequence of a ground fault can be irreversible damage to the stator core. For this reason, the diagnosis of this type of fault is of the greatest importance at the initial stage of fault, i.e., even at the stage of short-circuit between individual turns in a given phase of the winding. The protection systems currently used in drive systems may not react to the incipient stage of PMSM stator winding faults, because shorting a small number of turns causes too little quantitative change in phase currents, despite the very high levels of current flowing in the shorted part of the winding.

Methods for diagnosing faults in the stator windings of AC motors have been continuously developed for many years. In the last decade, special attention has been paid to new methods of fault diagnosis and the condition monitoring of PMSMs. Based on the recent literature-review papers, it can be concluded that the problem of fault diagnosis of PMSM stator windings has been widely developed by many research centers around the world [17,18,19,20,21,22]. There is an intensive search for both methods of extracting the symptoms of this type of fault at an early stage of its propagation, and methods of automating the process of its detection and classification.

Observing the trends in the development of PMSM fault diagnosis methods, it can be seen that mainly three approaches have been developed over the years [18]:

• Fault diagnosis based on mathematical models;
• Fault diagnosis based on analysis (processing) of diagnostic signals;
• Fault diagnosis based on artificial intelligence (AI) techniques.

However, it should be noted that nowadays, very often, methods based on mathematical models and those based on the analysis of diagnostic signals are combined with AI techniques.

Fault diagnosis based on mathematical models has gained significant popularity in recent years. These methods are based on analytical circuit models [23,24]; they involve analysis of the motor equivalent circuit, or field and field–circuit models [25,26], and are based on a more computationally complex finite element method (FEM). These are currently the two main directions for modelling phenomena occurring in electrical motors. Appropriately prepared models allow an analysis of phenomena occurring also in damaged motors. This makes it possible to analyze the impact of a given fault on simulated waveforms of diagnostic signals, such as stator current and voltage, electromotive force, or electromagnetic torque, among others. Nowadays, more and more advanced and detailed models of PMSMs with the ability to simulate various types of faults are being used to generate fault symptoms (features) to train AI-based models. This technique is one of the types of a rapidly developing approach—transfer learning (TF). This type of TF is called Model–Object TF. In this case, diagnostic methods are developed based on features preserved in diagnostic signals generated from mathematical models of the motors. Based on these, datasets used to train AI-based fault detectors and classifier models are prepared. These models can, in turn, be applied to real objects. Examples of such solutions for PMSM fault diagnosis are presented in [27,28,29,30]. Fault diagnosis based on mathematical models also reduces the cost of preparation and the interference with the design of motors under test caused by physically introducing a given fault. These are significant advantages of this approach. Model-based detection methods, however, require high computational power and accurate representation of machine parameters, the latter of which can be difficult to determine under real-world conditions. Methods that are based on motor models also include those based on the estimation of selected motor parameters, such as stator resistance, stator inductance, or flux linkage [31,32]; these may deviate from reference values after a fault occurs, and fault diagnosis and condition monitoring can be carried out based on the monitoring of this difference [33].

Fault diagnosis based on the analysis (processing) of diagnostic signals is currently the dominant approach. Such methods make it possible to monitor the condition of motors during operation on the basis of changes occurring in diagnostic signals as a result of the fault. They rely on the use of various mathematical apparatuses (signal processing methods) to extract symptoms of a given fault. Then, based on the values associated with these symptoms, an assessment of the technical condition of the motor is made. The most popular diagnostic signals combined with this approach include stator phase voltage [34] and current [35], temperature [36], active and reactive power [37], vibration [38] and axial flux [39]. For stator winding faults, the most commonly used diagnostic signals are the stator phase current and the voltage induced in the measuring coil by the axial flux. Due to the lack of a need to install additional coils and the non-invasiveness of the measurement, the use of the stator phase current signal is currently the dominant method [35,40,41].

The most common signal analysis methods used in the diagnosis of PMSM stator winding faults are those that realize frequency domain analysis. Signal analysis in the frequency domain implemented using the fast Fourier transform (FFT) algorithm is a basic and still widely used diagnostic approach. In this approach, the changes in the amplitudes of the fault-specific frequency components in the spectrum of the analyzed diagnostic signal are monitored. In the case of diagnosis of ITSCs in the PMSM stator winding, FFT spectrum analysis of the stator phase current signal is particularly popular. This method is known in the literature as Motor Current Signature Analysis (MCSA). It has been successfully applied and described in [35,40,42,43], among others. One improvement of the MCSA method is the spectral analysis of the stator phase current space vector module—Extended Park’s Vector Analysis (EPVA) [44,45,46]. Symptoms of stator winding faults are also evident in the FFT spectrum of the stator phase current envelope, as proven in [35]. ITSCs also cause changes in the spectrum of the instantaneous values of the symmetrical components of the stator phase currents [47]. Frequency-domain analysis methods with higher computational power requirements are based on higher-order transforms (HOTs). Among the most popular HOTs in the field of AC motor fault diagnosis are multiple signal classification (MUSIC) [48] and bispectrum (BS) analysis [49,50].

Despite their many advantages and proven effectiveness in extracting the fault symptoms of PMSM stator windings, methods that implement frequency-domain analysis have limitations, such as the loss of information about the time of occurrence of a given component after the transition from the time-domain to the frequency-domain. These disadvantages are devoid of methods implementing time-frequency domain analysis. They also allow the analysis of non-stationary signals. A prevalent method of time-frequency analysis in electric motor fault diagnosis is the Short-Time Fourier Transform (STFT). It has also been verified as to its applicability to electrical fault diagnosis in PMSMs [51,52]. The limitation of this method is the need to select an appropriate window size to obtain sufficient resolution in the time and frequency domains. Among the methods used to realize the time-frequency analysis used in the fault diagnosis of PMSM stator windings, and one using an adaptive time window, providing good resolution in both time and frequency domains, is the Continuous Wavelet Transform (CWT). Its effectiveness in extracting the ITSC symptoms has been verified in [53]. Another time-frequency analysis approach applied to PMSM electrical fault extraction is the Hilbert–Huang Transform (HHT) [54]. HHT combines the advantages of using Empirical Mode Decomposition with those of Hilbert spectral analysis. A novel LSPMSM stator winding fault diagnosis method based on time-frequency analysis based on the Gabor transform was proposed in [55]. The results presented by the authors proved that the method is effective and useful for detecting ITSCs in LSPMSM stator winding in transient states of the drive system.

Methods that implement time-domain analysis are mainly based on statistical analysis of the signal. They rely on signal parameters such as minimum, average, peak, kurtosis, or RMS. In [56], a method for diagnosing this type of damage was proposed based on an analysis of the normalized average value between the estimated and the reference value of the back electromotive force. The authors in [57] proposed a method based on the analysis of one-dimensional local binary patterns of the raw signals of stator currents and voltages (1D-LBP) to extract symptoms of ITSCs in the PMSM stator winding. Nevertheless, the fault detection efficiency obtained by the authors was only 80%. In turn, in [58], a statistical data analysis method called Principal Component Analysis (PCA), as applied to stator phase currents in the α-β coordinate system, is presented.

The fault symptoms extracted using the above-mentioned signal processing methods can be applied to train AI-based fault detector and classifier models. Fault detection based on AI techniques is currently the fastest-growing area of AC motor fault diagnosis. Such diagnostic methods allow full automation of the fault detection and classification process. They act as a decision-making block which generates information about the technical condition of the motor based on the raw diagnostic signal or symptoms extracted using signal processing methods. The development of intelligent methods for monitoring the condition of PMSM stator winding is still undergoing intensive development. These methods are typically based on classical machine learning (ML) algorithms, and artificial neural networks (ANNs). Among the most popular of the classical ML algorithms are algorithms such as K-Nearest Neighbors (KNN), Naive Bayes Classifier (NBC), and Support Vector Machine (SVM). They have also found application in the classification of PMSM stator winding faults.

The least computationally complex of the aforementioned classical ML algorithms is the KNN algorithm. Its effectiveness in the field of PMSM stator winding fault diagnosis has been verified in [47,53,59], among others. Another of the classic ML algorithms is NBC. This algorithm is also characterized by simplicity of implementation. Nevertheless, due to its limited effectiveness in fault diagnosis, it has not gained significant popularity there. The last, and computationally most complex, of the classical ML algorithms is SVM. Its high effectiveness in classifying the PMSM stator winding conditions has been verified and confirmed in application to stator winding fault detection in [59]. ML algorithms inspired by the principles of the human brain include classical (shallow) ANNs. The array of these methods that have been applied in PMSM faults diagnosis include MultiLayer Perceptron (MLP) [60], Radial Basis Function Neural Networks (RBF) [38] and Self-Organizing Maps (SOM) [26], among others. Nevertheless, deep neural networks (DNNs), which are based on deep learning (DL), have attracted the most interest in recent years. The reason for the rapid increase in the popularity of DL is the very high effectiveness of this technique in solving various types of problems that neural networks of shallow structure cannot optimally solve. The use of Convolutional Neural Networks (CNNs) currently dominates the aforementioned structures. The application of CNNs in fault diagnosis of AC motors is a relatively new, yet still intensively developed, issue. CNNs have also been applied in the fault diagnosis of PMSM stator winding [29,50,61,62]. Other structures based on DL are also known in the field of AC motor fault diagnosis. One of them is Long Short-Term Memory (LSTM), which is an extension of the idea of recurrent neural structures to DL techniques; the effectiveness of these approaches has been verified in the task of fault diagnosis of PMSMs [63,64]. Due to the much larger number of parameters of the LSTM model, compared to CNNs, and the more difficult learning process, they are mainly used in time sequence analysis.

However, most of the fault diagnosis solutions presented in the literature have been described based on results obtained using high-budget and high-performance computer diagnostic systems, the price of which often exceeds the cost of the motor to be monitored. They typically require an industrial PC, a high-precision data acquisition card, and costly software, such as LabVIEW or MATLAB, for proper operation. The cost of such a system can be as high as tens of thousands of dollars (USD). For this reason, the real chances of their industrial implementation are diminishing. Low-cost solutions are very rare in the literature and are mainly concerned with fault diagnosis of induction motors [65]. To address this, this article pays particular attention to analyzing the feasibility of using an embedded system based on a low-cost microcontroller with an ARM Cortex-M core to monitor the condition of the PMSM stator winding. The cost of the proposed system is about USD 100.

Embedded systems are used in most modern electronic devices. In recent years, embedded systems using microcontrollers have been the most widespread. They allow applications to achieve a high degree of compactness, responding to the increasing demands for the greatest possible miniaturization of devices. Microcontrollers are small single-chip microcomputers equipped with peripherals such as analog-to-digital (ADC) and digital-to-analog (DAC) converters, timers, comparators, communication interfaces, memory, etc. In recent years, microcontrollers with ARM Cortex-M core have become particularly popular [65]. The Cortex-M family is based on the ARM architecture.

This paper aims to analyze the feasibility of implementing an ML-based PMSM stator winding fault diagnosis algorithm on a low-cost microcontroller and to develop an embedded diagnostic system for monitoring the condition of the winding during drive system operation, aiming to reduce the cost significantly. It assumes that there is no need for high-end data acquisition cards and industrial PCs. The LESR 6-NP (LEM, USA) current transducers-based PCB is configured to measure PMSM stator phase currents, which are used as diagnostic signals. The signal acquisition and processing process is realized using the NUCLEO-H7A31ZI-Q (STMicroelectronics, Plan-les-Ouates, Geneva, Switzerland) module with a 32-bit ARM-Cortex M7-based STM32H7A31ZI microcontroller. The developed system also avoids the significant cost of purchasing software licenses (cf., LabVIEW and MATLAB) since the fault detection algorithm is written in a general-purpose C programming language. The method of measuring the diagnostic signal, its acquisition, the implemented KNN-based fault classifier, the components of the prepared system and the necessary configuration are also discussed in detail.

The main contributions of this research are as follows:

(1)

Development of a printed circuit board with LESR 6-NP current transducers for measuring the PMSM stator phase currents, which are used as diagnostic signals.

(2)

A detailed description of the process of configuration of the stator phase current signal acquisition and processing using a low-cost microcontroller.

(3)

Detailed analysis of the effect of ITSCs in the PMSM stator winding on the waveform and FFT spectrum of the stator phase current space vector module.

(4)

Implementation of an ML-based method (KNN model), for condition monitoring and classification of PMSM stator winding faults, on a low-cost microcontroller.

(5)

Preparation of the concept and experimental verification of the low-cost microcontroller-based system for condition monitoring of the PMSM stator windings.

(6)

Critical analysis of the effectiveness of the developed low-cost PMSM stator winding condition monitoring system.

The rest of the article is organized as follows: Section 2 describes the key parameters of the developed stator phase current measurement PCB. Section 3 presents the experimental setup components and configuration of the data acquisition. In Section 4, PMSM stator winding fault diagnosis method and experimental verification results are provided. Section 5 concludes the paper.

2. Key Parameters of the Developed Stator Phase Current Measurement PCB

The key components of the proposed low-cost microcontroller-based PMSM stator winding condition monitoring system are a circuit board for measuring stator phase currents and the NUCLEO-H7A3ZI-Q board responsible for acquiring and analyzing diagnostic signals, generating inferences about the motor condition. An illustrative diagram of the diagnostic information processing using the developed concept is shown in Figure 2. In this section, the key parameters of the developed stator phase current measurement PCB are described.

As AC motors are current-driven devices, precise and quick measurement of phase currents is crucial for fault diagnosis. This AC is generated from DC voltage by PWM signals, which inevitably generates high voltage rise and fall times, which, in turn, is a source of electrical noise. A substantial majority of motor inverters are powered directly from the rectified mains voltage, and no galvanic isolation is provided. Hence, motor phases usually carry dangerous voltages and the potential between individual motor phases is constantly changing. Because of these reasons, a current sensor with galvanic isolation between measured potential and data output is strongly preferred. In addition, since the frequency components to be analyzed can be multiples of the power supply frequency, sufficient current sensor bandwidth is required to properly detect signs of faults among the aforementioned electrical noise. For the purposes of this research, the low cost of the system and ease of integration with a microcontroller are also very important.

Ready-to-use systems meeting most of the above requirements are easy to find, sometimes in the form of an oscilloscope with current probes or a professional data acquisition system, but usually the cost of such solutions is very high, and still, not every requirement is met, like ease of integration. For these reasons, a simple but effective custom PCB tailored to the needs of this research was designed. The main element of such a board is the current sensor itself. There are several ways to measure current, but one of the most common sensor types is a Hall Effect sensor. From an electronic design point of view, commonly found products are either open-loop or closed-loop Hall Effect sensors. As the latter offers higher accuracy, it was chosen. Galvanic isolation is crucial for the safety of the user, but thankfully such sensors are usually designed in a way that a sufficient separation between “hot” and “safe” parts of the circuit is provided. For good accuracy, the measuring range of the sensor should be close to the maximum expected measured value, and have a sufficient margin. Motor currents are bipolar (flowing in both positive and negative directions), but microcontroller-based systems usually are unipolar, with +3.3 V/+5 V voltage rails available. Therefore, the measuring component should output the data either through a digital interface or as an analog signal, with common mode voltage, which is equivalent to zero current flowing through the sensor, set somewhere between 0 V and 3.3 V, a common range for measured voltage in ADCs found in modern microcontrollers. With all these requirements in mind, a LESR 6-NP LEM’s current transducer(LEM, Meyrin, Switzerland) was chosen. The key electrical parameters of this sensor can be found in

Table 1. Key electrical parameters of the LESR 6-NP current sensor.

Parameter	Value
Supply voltage	4.75…5.25 V
Bandwidth (±1dB)	300 kHz
Primary current, measuring range	±20 A
Creepage/Clearance distance	7.55 mm
Reference voltage = Common mode Voltage UEref range	0.5…2.75 V
Output voltage UOut	0.25…4.75 V
Sensitivity SN	104.2 mV/A
Maximum error	0.45%

. Detailed specifications are available on the manufacturer’s website [66].

One of the significant advantages of the LESR transducer series is the possibility of supplying an external reference voltage, an option which is very convenient when interfacing with a simple ADC module. As mentioned previously, the common mode voltage of the analog output signal of the sensor should be in the 0…3.3 V range, ideally in the middle (1.65 V), to obtain a maximum resolution for the signal. Knowing the common mode voltage, sensitivity, and output voltage range, a practical measurement current range can be calculated using the following formulas [66]:

$$I_{LowerLimit}={\displaystyle \frac{u_{OutMin}-u_{Eref}}{S_N}}={\displaystyle \frac{0.25 \mathrm{V}-1.65 \mathrm{V}}{104.2 \mathrm{mV}/\mathrm{A}}}=-13.44 \mathrm{A},$$

(1)

$$I_{UpperLimit}={\displaystyle \frac{u_{OutMax}-u_{Eref}}{S_N}}={\displaystyle \frac{4.75 \mathrm{V}-1.65 \mathrm{V}}{104.2 \mathrm{mV}/\mathrm{A}}}=29.75 \mathrm{A},$$

(2)

where u_OutMin and u_OutMax are the minimum and maximum output voltages of the LESR 6-NP current transducer, and I_LowerLimit and I_UpperrLimit are the minimum and maximum allowed primary current, respectively.

To set the U_Eref, a low-impedance signal is required. The easiest way to generate it is to use a symmetric resistor divider to divide the 3.3 V rail, which is also the reference voltage for the ADC, in half—thereby creating a 1.65 V reference. This signal is then buffered using an operational amplifier. A schematic of this solution is presented in Figure 3. As the 1.65 VREF signal is a static one, the main requirement of the operational amplifier is low input offset voltage. For this reason, the MCP6V02E amplifier was chosen.

For easy connection to a microcontroller development board, a standard 2.54 mm gold-pin connector was used. Additionally, two LEDs were placed on the PCB to verify the connection of the 3.3 V and 5 V rails. The analog signals from the LESR sensors are routed through a small RC filter composed of a 100 Ω resistor and 100 pF capacitor, giving a cut-off frequency of about 16 MHz. As the LESR outputs are low-impedance, there is no need for additional buffering. The board is intended to be a universal one, so for the motor connection, high-current, screw-type terminals were used. For AC motors, shielding of the motor cable is common, so a fourth terminal is present on the PCB for easy routing of the wire harness and potentially for additional shielding. A small disadvantage of the design is that the motor phase order is reversed, but the appropriate warning is printed on the board silkscreen. This inversion was necessary to achieve a good layout on a two-layer PCB. Additional supply filtering capacitors and holes for mechanical support are also included in the design. The final PCB layout, in addition to a photograph of the developed measuring board, is presented in Figure 4.

3. Experimental Setup Components and Configuration of the Data Acquisition

3.1. Development Board and Microcontroller Used

The acquisition and processing of the stator phase current signals measured using the PCB described in the previous subsection, as well as ML-based automatic inference of the winding condition, is performed using the NUCLEO-H7A31ZI-Q development board by STMicroelectronics. This board provides an affordable and flexible way to try new concepts and cost-effectively build prototypes and is designed to develop new applications efficiently. The key component of this board is the STM32H7A3ZI 32-bit microcontroller. The NUCLEO-H7A31ZI-Q also integrates the STLINK-V3E debugger/programmer and does not require separate probes. It has 3 user LEDs and 2 user and reset push buttons on the board. There are 3 flexible power supply options: ST-LINK USB VBUS, USB connector, and other external sources. Moreover, support for a wide selection of integrated development environments (IDEs) is provided, including the STM32CubeIDE used in this work. An actual view of the NUCLEO-H7A31ZI-Q board, with its key components highlighted, is shown in Figure 5.

The STM32H7A3ZI-Q microcontroller used is in a 144-pin LQFP144 package. It is based on the ARM Cortex-M7 core, which operates at a frequency of up to 280 MHz. ARM Cortex-M processors are currently one of the best choices, and are seen in a wide range of applications. ARM reported a record 4.4 billion Cortex-M processor chips sold in the fourth quarter of 2020, confirming the chip’s very high popularity. These microcontrollers are characterized by high reliability, strong performance and affordability [67,68]. The STM32H7A3ZI-Q microcontroller has a double-precision floating point unit (FPU) and provides 2 Mbytes of flash memory and 1.4 Mbytes of SRAM memory. It features 5 Direct Memory Access (DMA) controllers to unload the CPU during operation. Key peripherals of this microcontroller include two 16-bit ADC modules, each supporting up to 24 channels, and two 12-bit DACs. It also supports a set of Digital Signal Processing (DSP) instructions which allow efficient signal processing and execution of the complex algorithms. It provides communication peripherals such as I2C, USARTs, SPIs and CAN. These features make the STM32H7A3ZI-Q microcontroller suitable for various applications, including the condition monitoring and fault diagnosis of AC motors. Nevertheless, the price of this microcontroller is slightly higher than those of microcontrollers with lower computing power also from the ARM family (e.g., with M0, M3 or M4 cores). In the future, it is planned to study the possibility of developing diagnostic methods on microcontrollers of even lower cost. The key features of this microcontroller are grouped in

Table 2. Key features of the STM32H7A31ZI-Q microcontroller.

Feature	Details
Core	ARM Cortex-M7 (32-bit)
Operating clock frequency	Up to 280 MHz
FPU	Double-precision
Flash memory	2 Mbytes
SRAM memory	1.4 Mbytes
DMA	5 × 16-channel
ADC	2 × 16-bit
DAC	3 × 12-bit
Timers	2 × 32 bit, 15 × 16 bit
Communication peripherals	4 × I2C, 5 × USART, 6 × SPI, 2 × CAN, 2 × SAI

.

3.2. Motor Test Bench

The developed low-cost microcontroller-based PMSM condition monitoring system was verified on a motor test bench that consists of two Lenze PMSMs. The main test object was a 2.5 kW PMSM. The design of the windings of its stator had been specially prepared so that it was possible to physically short-circuit a given number of turns in phase by connecting the taps corresponding to the group of coils leading out to the terminal board. The key parameters of this PMSM are listed in

Table A1. Rated parameters of the tested PMSM.

Name of the Parameter	Symbol	Units
Power	PN	2500	[W]
Torque	TN	16	[Nm]
Speed	nN	1500	[r/min]
Stator phase voltage	UsN	325	V
Stator current	IsN	6.6	[A]
Frequency	fsN	100	[Hz]
Pole pairs number	pp	4	[−]
Number of stator turns	Nst	2 × 125	[−]

(Appendix A). This motor was coupled to a second PMSM, rated at 4.7 kW, which provided the load torque. Both motors were powered by voltage source inverters (VSIs). The PMSM motor under analysis operated in a closed-loop field-oriented control (FOC) structure. The inverter of the second PMSM operated in torque-control mode. Figure 6a shows an overview of the motor test bench, and Figure 6b presents an illustrative diagram of the terminal board of the PMSM with specially prepared construction of the winding. Each phase of the PMSM stator winding has two coils of 125 turns each. During the experimental verification, the incipient stator winding faults were analyzed. The minimum number of shorted turns (N_sh) was one, which represents 0.4% of all turns in a phase. Short-circuits in the range of 1 to 5 were analyzed. ITSCs were physically modelled by connecting the taps corresponding to a given N_sh leading out to the terminal board through a metallic connection with a wire—without additional resistance in the short-circuit loop to lower the short-circuit current (R_sh ≈ 0 Ω).

The stator phase current signals were measured using a developed PCB with LESR 6-NP current transducers. The data acquisition and processing were based on the NUCLEO board already described. The application was written in the C programming language. The stator phase currents were also recorded using a Siglent (Siglent, Helmond, the Netherlands) SDS2104X digital oscilloscope and PINTEK (PINTEK, Shulin, New Taipei City, Taiwan) PA-655 current probes (Figure 6c). Motor control was realized in the Lenze (Lenze, Aerzen bei Hamelb, Germany) Engineer software (v2.30) application, while load torque was controlled in Veristand. The microcontroller’s pin configuration was realized using the Integrated Development Environment (IDE) developed by STMicroelectronics (STMicroelectronics, Plan-les-Ouates, Geneva, Switzerland) (STM32CubeIDE v1.16.0).

3.3. Configuration and Verification of the Stator Phase Current Signals Measurement and Acquisition

The key to the correct operation of the system under development is the proper acquisition of the diagnostic signal. To implement it, it is necessary to properly configure the ADC module of the microcontroller which will process the output voltage from the measuring PCB, at a setting proportional to the flowing PMSM stator phase currents. For this purpose, three channels of the 16-bit successive approximation ADC2 module were used in single-ended mode. The three output voltage signals from the measuring PCB (OUT_U, OUT_V and OUT_W) were connected to the PA7, PA6 and PF14 pins of the microcontroller, which correspond to channel 7 (ADC2_INP7), channel 3 (ADC2_INP3) and channel 6 (ADC2_IN6) of the ADC2 module. The interface between the stator current measuring module and the data acquisition and processing module is presented in

Table 3. The interface between the measuring module and the data acquisition and processing module.

Measuring PCB Pin ID	Measuring PCB Pin Function	µC Pin ID	µC Pin Function
+3.3 V	3.3 V supply input	3V3	3.3 V supply output
+5 V	5 V supply input	5V	5 V supply output
OUT_U	LEM’s output voltage proportional to the current in phase U (A)	PA7	ADC2 module input of channel 7 (ADC2_INP7)
OUT_V	LEM’s output voltage proportional to the current in phase V (B)	PA6	ADC2 module input of channel 3 (ADC2_INP3)
OUT_W	LEM’s output voltage proportional to the current in phase W (C)	PF14	ADC2 module input of channel 6 (ADC2_INP6)
GND	Ground	GND	Ground

. The illustrative diagram and a photograph of this interface are presented in Figure 7.

The timer 1 (TIM1) is used to trigger the ADC2 conversion. It generates a cyclic interrupt at every specified time, counting from 0 to the value defined in the AutoReload Register (ARR). The ADC sampling frequency (f_p) equals the frequency of the TIM1 interrupt triggering. It can be calculated according to the following equation:

$$f_p={\displaystyle \frac{f_{CLK\_BUS}}{TIM\_ARR+1}},$$

(3)

where f_{CLK_BUS} denotes the clock frequency of the bus on which the TIM1 is located, and TIM_ARR is the value written in the ARR register.

TIM1 is located on the APB1 bus clocked at 64 MHz. Therefore, the f_{CLK_BUS} = 64 MHz. Assuming a sampling frequency of 4 kHz, the value written in the ARR register is

$$TIM\_ARR={\displaystyle \frac{f_{CLK\_BUS}}{f_p}}-1={\displaystyle \frac{64\cdot 10^6\mathrm{Hz}}{4000 \mathrm{Hz}}}-1=15999.$$

(4)

Once the conversion is complete, the DMA regularly transfers the result of the conversion to a buffer in a DMA circular mode, in order to unload the CPU during operation.

In the ADC2 interrupt subroutine, the acquired output voltages of the LEM current transducers are read and assigned to the uint16 variables (ui16OUT_U_Raw_Digital, ui16OUT_V_Raw_Digital, ui16OUT_W_Raw_Digital). The largest digital value that can be output by the 16-bit ADC2 is determined by the number of unique values it can represent. For the 16-bit ADC, it is 2¹⁶ distinct values. The maximum decimal digital value that can be read is 65,535 (the counting is started from 0). The raw digital values read by the ADC when no currents flowed through the measuring PCB are shown in Figure 8.

To convert the raw digital ADC value into a measured voltage proportional to the stator phase currents, the following calculations are required [69]:

$$V_{ACTUAL\_ADC}={\displaystyle \frac{V_{REF+}}{FULL\_SCALE}}\cdot ADC\_VALUE,$$

(5)

where V_{ACTUAL_ADC} is the voltage measured by the ADC module, V_REF+ is the reference voltage value of the ADC module (V_REF+ = 3.3 V), ADC_VALUE is the digital value converted by the ADC module and FULL_SCALE is the maximum digital value of the ADC output equal to FULL_SCALE_16-bit = (2¹⁶ − 1) = 65,535. Figure 9 shows the waveforms of the measured voltages (fOUT_U_Voltage, fOUT_V_Voltage, fOUT_W_Voltage float type variables) after converting the raw digital ADC value according to Equation (5). As was expected based on the description of the stator phase current measurement PCB properties (Section 2), the output value of this PCB, when no current is flowing, is around 1.65 V. The additional ≈ 25 mV offset is visible due to the components’ tolerances.

The final step is to convert these voltages to obtain the stator phase currents in amperes. To do so, it is necessary to subtract the offset and take into account the sensitivity of the current transducers used (104.2 mV/A):

$$I_s={\displaystyle \frac{V_{ACTUAL\_ADC}}{S_N}}-OFFSET,$$

(6)

where I_s is the calculated stator phase current proportional to the output voltage, and OFFSET is the offset to be subtracted. All of the above conversions are done in the ADC2 interrupt subroutine called each 250 μs.

The waveforms of the calculated stator phase currents (fMeasuredCurrent_U, fMeasuredCurrent_V, fMeasuredCurrent_W float type variables) in the absence of current flow are shown in Figure 10. In this case, the value is close to 0, confirming that the measurement and acquisition configuration is correct.

Stator phase current waveforms recorded with an oscilloscope and measured and acquired using the developed low-cost condition monitoring system are shown in Figure 11a and Figure 11b, respectively. These waveforms correspond to the operation of the tested PMSM at the rated load torque. Based on the comparison of these waveforms it can be concluded that the measurement and acquisition of the diagnostic signals are plausible.

4. PMSM Stator Winding Fault Diagnosis Method and Results

4.1. PMSM Stator Winding Fault Symptom Extraction

A key part of the preparation of condition monitoring and fault diagnosis systems is the selection of fault features based on the determination of which inference of motor condition will be carried out. To accomplish this, it is necessary to analyze the impact of a given fault on the diagnostic signals. Symptoms of PMSM stator winding faults can be seen not only in the stator phase current signal [35], but also in the stator phase current space vector module |i_s|, as proven in [52], among others. The waveforms of these signals for different T_L levels, and momentary short-circuiting successively from 1 to 5 turns in the PMSM stator winding, are presented in Figure 12. Based on the analysis of these waveforms it can be concluded that the ITSCs cause increases in the values of the amplitude of both the stator phase currents and their space vector module. More apparent is the effect on |i_s|, confirming previous studies [52]. In addition, the effect of the load torque on the current amplitude is visible, and increases as the load torque increases.

As mentioned at the beginning of the paper, the effect of a short-circuit on the value of phase current amplitudes is not significant. Nevertheless, in a shorted part of the winding, a current with a much higher amplitude flows. In the case of one shorted turn, the value of the amplitude of the current reached about 25 A. For two shorted turns, it was already 50 A. The waveforms of the stator phase current in a phase in which a short-circuit was introduced (pink curve), and the current that flowed in the shorted part of the winding (yellow curve) for two shorted turns in the PMSM stator winding are presented in Figure 13.

The effects of the N_sh in the PMSM stator winding and the T_L level on the average value (averaging done on 1024 samples) of the stator phase current space vector module are presented in Figure 14. From an analysis of the bar graphs shown in this figure, it is possible to notice the effect of the fault on the |i_savg|, which appears as an increase in its amplitude. However, the increase due to an increase in the load torque is much greater, so it is not possible to use this indicator alone to assess the condition of the winding. For the unloaded motor, T_L = 0.6T_N, and T_L = T_N, the average amplitudes of |i_savg| equal 0.45 A, 5.63 A and 9.05 A, respectively. The percentage increase as a result of the ITSC for N_sh = 1 and T_L = 0 reached ≈6.3%, but for T_L = T_N it dropped to only ≈0.5%.

To extract the fault indicators more sensitive to the fault to be used as input values for the AI-based fault classifier model, the |i_s| waveform was processed using FFT. The FFT spectra of this signal for undamaged stator winding and two different ITSC fault severities (N_sh = 2, N_sh = 4) are shown in Figure 15. The increase of the second harmonic amplitude (2f_s) is visible as a result of the PMSM stator winding fault. The changes in the amplitude of this component were analyzed in detail for a wider range of N_sh and T_L levels to make a final assessment of their sensitivity to the fault. The result of this analysis is presented in Figure 16. Based on this, it can be concluded that the amplitude increase as a result of the ITSC fault is visible in the full range of the T_L.

4.2. Automation of PMSM Stator Winding Fault Diagnosis

Automation of condition monitoring and fault diagnosis is essential for modern drive systems. This is most often implemented using AI techniques, including ML algorithms. For the development of low-cost embedded systems, implementing ML algorithms can be a challenge, one which will also be addressed in this work.

The input vector of the fault classifier model was constructed based on the analysis performed in the section on the ITSC symptom extraction process. The first element of this vector is the average value of the stator phase current space vector module |i_savg|. The averaging is performed on 1024 samples. The |i_savg| carries information about the level of T_L. The second element is the amplitude of the 2f_s component in the FFT spectrum of this signal, which is sensitive to the PMSM stator winding fault. The dataset used to train and test the model consists of 360 vectors. They correspond to different PMSM stator winding conditions (N_sh = {0; 1; 2; 3; 4; 5}) and load torques (T_L = {0; 0.2T_N; 0.4T_N; 0.6T_N; 0.8T_N; T_N}). In all, 70% of the vectors (252) were used in the training process and the remaining 30% (108) in offline tests. The dataset was balanced—for each of the T_L and N_sh, 10 vectors were included. Due to the simplicity and proven high effectiveness in the PMSM stator winding fault diagnosis task [47,52,53], the KNN algorithm was selected for use in the developed low-cost condition monitoring and fault diagnosis system.

4.2.1. K-Nearest Neighbors

The KNN algorithm is a simple and efficient ML algorithm widely used in the field of data classification [70]. To classify unknown data represented by a new input feature vector as a point in the feature space, the KNN model calculates the distance between the new point and the points used in the training process. The classifier then assigns the point to the class to which most of points among its K nearest neighbors belong. K is a hyperparameter of the KNN model and is an integer value to be determined in advance [47]. Various metrics for calculating the distance between adjacent points are described in the literature. The most common is the Euclidean distance, calculated according to the following equation [71]:

$$d_{Euclidean}(A,B)=\sqrt{{\displaystyle \sum _{i=1}^n(x_i}-y_i{)}^2},$$

(7)

where

• x_i, y_i—elements of the A and B feature vectors, respectively;
• n—feature space dimension.

The pseudocode of the KNN algorithm is presented in Algorithm 1 [72]. The embedded implementation of this algorithm was based on the Edge Learning Machine (ELM) ML framework proposed in [73] and dedicated to ARM microcontrollers. This framework manages the learning phase on the desktop computer and performs inference on the microcontrollers. Like the rest of the software of the diagnostic system under development, the implementation of this algorithm is performed in the C programming language.

Algorithm 1: The pseudocode of the KNN algorithm

Data: D = {Xi,ci}, for i = 1 to N, where X i = (x1, x2…, xm) is an m-element input vector that belongs to class ci, N is the number of elements contained in the dataset.   Data: A = (a1, a2…, am) new data to be classified   Result: class to which the new input vector A belongs   Initialize distances[N] ← {0};   for Xi in D do   di ← d(Xi, A);   distances [i] ← di;   end   Sort distances {di, for i = 1 to N} in ascending order;   Get the first K cases closer to A (with the smallest distance), $D_A^K$;   class ← most frequent class in $D_Z^K$;

4.2.2. Results

The accuracies of the KNN model for different K values for the training dataset are presented in Figure 17. The highest accuracy equal to 100% was achieved for the K = 3 and K = 5. Considering the simplicity of the model and the reduction in response time, K = 3 was chosen for further testing. The responses of this model for the vectors that were included in the test dataset are shown in Figure 18. The classification effectiveness (the ratio of the correctly classified vectors to the sum of the vectors in the dataset) of this test equals 97.2%.

In the next step, the pre-trained model was integrated into the microcontroller and the developed system was verified during the online tests on the motor test bench. The stator phase current and stator phase current space vector module waveforms, as well as the responses of the KNN model, are presented in Figure 19. The achieved classification effectiveness for this test was 95.94%, which is a satisfactory result.

The real-time data from the microcontroller memory for debugging and evaluation purposes was monitored using the STM Studio v3.6.0 software. Nevertheless, the developed diagnostic system can operate without additional devices, e.g., a PC. To pass the information about the stator winding condition to the other components of the systems, such as the inverter, one of the microcontroller outputs (GPIO port F, pin 8—PF8) is set to true (pulled up to 3.3 V) if the fault occurs (when the predicted number of shorted turns is higher than 0). The output can then be used to light an LED to indicate a fault or to activate a buzzer that will sound in the event of a fault. A simplified illustrative block diagram of the implemented program is shown in Figure 20.

To accurately assess the timing of fault detection and further evaluate system performance, the PMSM stator phase current waveforms and the output indicating the stator fault (green curve) were recorded with an oscilloscope during a test in which five turns were shorted after a period of normal operation under rated conditions (Figure 21). The system recognized the fault 289 ms after it occurred. In the final step, only the response time (time needed to classify new vectors by the KNN algorithm) of the KNN model was measured using an oscilloscope. The response time is the time when the additionally configured output was held high (at 3.3 V). The result is presented in Figure 22. Based on the analysis of this waveform, it can be concluded that the response time is only 6.25 ms.

5. Conclusions

The concept presented in this paper and its verification have confirmed the feasibility of developing an effective, low-cost PMSM stator winding condition monitoring and fault diagnosis system. The developed system achieved a high fault classification effectiveness of more than 95% during online tests on the motor test bench. It meets the premised requirement of not needing high-end data acquisition cards and industrial computers. The cost of the system is only about USD 100.

The developed stator phase current measurement PCB made it possible to measure the diagnostic signal over the full load torque range of the PMSM under analysis. The presented design could be a starting point for further development and integration. Some minor improvements like better filtering, or routing the reference voltage signal to the connector to have a semi-differential measurement, could be easily made with negligible impact on the final cost. LESR 6-NP current transducers are designed for use in AC motor inverters. Successfully using them to detect faults in the motor winding is proof that such features could be integrated on the inverter PCB level.

The acquisition and processing of diagnostic signals were successfully implemented in a low-cost embedded system (NUCLEO-H7A3ZI-Q) with an ARM Cortex-M7 core. An ML model based on the KNN algorithm for automatic detection and classification of PMSM stator winding faults was successfully implemented, achieving a short ITSCs detection time. The diagnostic information carried by the stator phase current space vector module signal was used as input vector elements of this model.

A detailed description of the process of configuring measurement, acquisition, and processing of a diagnostic signal using a low-cost microcontroller can be helpful in the development of embedded diagnostic systems designed for various fields. Implementation of a method for condition monitoring of PMSM stator winding faults on a low-cost microcontroller is rare in the literature. Therefore, this work can make an important contribution to this field. Moreover, the module can be installed non-invasively between the inverter and the monitored motor, which also increases its potential for industrial implementation.

Future research will focus on the development and implementation of other artificial intelligence techniques (ML-based algorithms) in the developed system and a comparative analysis of their effectiveness. The next step will also involve integrating the measurement board and the acquisition board on a single PCB and equipping the system with a display to indicate the winding condition. The system can also be extended to include algorithms for diagnosing other types of PMSM faults, which is made possible by its easy re-programmability.