Comprehensive Fault Diagnosis of Three-Phase Induction Motors Using Synchronized Multi-Sensor Data Collection

Abstract

Induction motors are critical to industrial operations but are prone to mechanical and electrical faults. This paper introduces a new dataset for comprehensive fault diagnosis of three-phase induction motors, featuring synchronized multi-sensor data collection. Real-time measurements of vibration, voltage, and current were captured from a 0.2 kW squirrel cage induction motor using high-resolution sensors, with all signals sampled at 50 kHz. Fault scenarios, including phase removal and mechanical misalignments, were simulated to capture diverse motor behaviors. The dataset, organized into ten distinct CSV files covering various operational states, provides a rich resource for developing and testing fault detection algorithms. A Random Forest classifier trained on this dataset achieved an accuracy of 99.82%, demonstrating its suitability for real-time fault diagnosis and predictive maintenance applications. Unlike existing datasets, this collection offers synchronized electrical and mechanical sensor data, enabling advanced cross-sensor fault analysis. The dataset is publicly available and aims to support researchers in advancing machine learning approaches for motor health monitoring.

Background & Summary

Induction motors are integral to modern industrial applications due to their efficiency, reliability, and relatively low maintenance requirements. Driven by vital machinery like compressors, conveyors, and pumps, they are the workhorses of the industry, utilized in a variety of systems from manufacturing lines to power plants. Motors consume over 50% of the electrical energy in industrial applications¹. For heavy-duty operations, their robust construction, straightforward design, and capacity to function in challenging conditions make them the preferred option. Induction motors can cause major operational disruptions due to faults and failures, even with their many benefits. Faults can cause decreased performance, excessive vibrations, overheating, and, in the worst cases, total motor failure².

Induction motors are prone to a range of faults, broadly classified into three categories: mechanical, electrical, and environmental. The rotor and stator windings are the main sources of electrical faults. Insulation failures are also common electrical faults. Studies proves that 30%–40% of all induction motor faults is related to stator winding fault³. Short circuits, open circuits, or deteriorating insulation within the windings are the usual causes of stator winding faults⁴. Overheating, mechanical strain, or chemical exposure can all cause insulation to deteriorate, which can result in short circuits between windings and decreased motor efficiency⁵. Unbalanced rotor currents can result from rotor faults like broken rotor bars and end-ring faults, which can stress and vibrate the motor⁶. Over time, insulation materials can deteriorate due to thermal, electrical, and mechanical stresses, resulting in phase-to-phase or phase-to-ground faults, which can significantly damage the motor⁷.

Mechanical faults in induction motors are often related to moving parts like bearings and the rotor⁸. The most frequent mechanical problems are bearing failures, which is caused by fatigue, poor lubrication, contamination, or misalignment⁹. Motor failures results from the increased friction and overheating caused by failed bearings¹⁰. Uneven stress distribution from shaft misalignment, occurs when the motor shaft and the driven load are not aligned properly, leading to vibrations, bearing wear, and leading to damages to the motor. Motor performance is greatly impacted by operational and environmental factors in addition to electrical and mechanical problems. Insulation can deteriorate and electrical faults can result from overheating, which is frequently brought on by overloading, inadequate ventilation, or high outside temperatures¹¹. Dust, dirt, moisture, or chemicals can contaminate a motor, impairing its performance and increasing the risk of corrosion, bearing failure, and insulation degradation¹².

To mitigate these risks, the early detection and diagnosis of motor faults have been subjects of extensive research. Research into sophisticated data collection techniques has been prompted by the significance of real-time monitoring and fault detection systems¹³. Research has evolved from manual, periodic inspections, which often overlook early-stage issues, to sophisticated, automated methods that leverage data-driven approaches and machine learning techniques¹⁴. It is possible to identify abnormal behaviors linked to faults like phase loss, bearing wear, rotor imbalances, and electrical imbalances by collecting and analyzing data on motor performance¹⁵. Motor diagnostics have traditionally depended on manual monitoring and recurring inspections, which frequently ignore early-stage issues and trigger unforeseen breakdowns¹⁶. Shady et al.³ has proposed a technique in detecting, and diagnosis of stator inter-turn short circuits and unbalanced supply voltages. They have presented a current-based method and supply voltages to detect the faults in squirrel cage induction motor. This approach illustrates the critical role that electrical signal monitoring plays in early fault detection, as faults often manifest as subtle anomalies in current patterns. This work was advanced by Raqeeb et al.¹⁷ by employing image-based methods to detect bearing faults using convolutional neural networks (CNNs). In order to maximize detection accuracy, they experimented with different image sizes, demonstrating the ability of CNNs to process intricate data representations to identify particular fault types. The advent of CNNs has created new opportunities for the diagnosis of motor faults, particularly when a visual examination of wear and tear is required. Similarly, Ma et al.¹⁸ achieved over 80% diagnostic accuracy by using CNNs to analyze current signals for fault detection. This demonstrates the efficiency of using deep learning models in conjunction with signal processing techniques to extract relevant features from unprocessed sensor data. By training on historical current signals, CNN models are able to identify patterns linked to motor malfunctions, facilitating preventative maintenance actions. At the University of Ottawa, Sehri et al.¹⁹ recorded a dataset that included temperature, vibration, and acoustic data related to electric motors. This publicly available real-world dataset is useful for researching the interactions between various parameters under fault conditions.

In order to identify faults in induction motors, Han et al.²⁰ created a deep learning algorithm that used vibration signals converted into images. This study emphasized the value of vibration analysis, which frequently identifies misalignments or mechanical imbalances in motor components. By treating vibration signals as image data, they demonstrated how machine learning techniques could be applied to non-traditional data formats in motor diagnostics. Choudhury et al.²¹ presented a related method for identifying faults in electric vehicle (EV) motors: acoustic signature-based transfer learning. Utilizing acoustic data, this method provides important insights into motor health, especially in settings where vibrations might not provide enough information for diagnosis. Transfer learning is especially helpful in situations where there are no large labeled datasets available. It allows models trained on one dataset to be adjusted for a different but related task. For wound rotor synchronous motors, Skowron et al.²² proposed an artificial neural network-based classification system that used current sensor data to identify faults. Their approach added to the body of research on sensor fault compensation, which aims to keep motor systems running even in the event of sensor malfunctions.

Several publicly available datasets have already been published, focusing on various aspects of motor fault detection and an overview of the most recent datasets is given in Table 1, which emphasizes that most of them come from Real-world datasets, but many of them are no longer accessible to the general public. More extensive datasets that capture a variety of fault types across a range of operational scenarios are still required, despite these valuable contributions. A large number of the current datasets focus to specific fault types, like bearing defects, rotor faults and frequently do not include information about simultaneous electrical and mechanical faults. In addition, these datasets typically lack real-time data synchronization between various sensor types, such as accelerometers for vibration monitoring and sensors for electrical parameters like current and voltage. For instance, the dataset provided by Treml et al.²³ features real-time data collected from multiple sensors, including acceleration, voltage, and current measurements. However, it focuses exclusively on a single fault type i.e. rotor faults limiting its scope for diagnosing a wider range of motor issues.

Table 1.

Overview of Published Datasets.

Reference	Dataset	Dataset Type	Availability	Recorded Parameters
Carletti et al.36	Synthetic Dataset For Induction Motor Broken Rotor Bar Analysis	Synthetic	Fully Not Accessible	Current
Raqeeb et al.17	Data-Driven Bearing Fault Diagnosis For Induction Motor	Real	Not Available	Bearing Image
Felipe37	Machinery Fault Dataset	Real	Available	Vibration
Ma et al.18	Fault Diagnosis Of Motor Bearing Based On Current Bi-Spectrum And Convolutional Neural Network	Real	Not Available	Current
Wonho et al.38	Vibration And Motor Current Dataset Of Rolling Element Bearing Under Varying Speed Conditions For Fault Diagnosis: Part 2	Real	Available	Vibration And Current
Sehri et al.19	University Of Ottawa Electric Motor Dataset – Vibration And Acoustic Faults Under Constant And Variable Speed Conditions	Real	Available	Vibration, Acoustic And Temperature
Treml et al.23	Experimental Database For Detecting And Diagnosing Rotor Broken Bar In A Three-Phase Induction Motor	Real	Not Available	Voltage, Current And Vibration
Baoquan39	Bearing	Real	Not Available	Not Mentioned
Han et al.20	Motor Fault Diagnosis Using Cnn Based Deep Learning Algorithm Considering Motor Rotating Speed	Real	Not Available	Vibration
Choudhary et al.21	Deep Transfer Learning Based Fault Diagnosis Of Electric Vehicle Motor	Real	Not Available	Vibration
Skowron et al.22	Current Sensor Fault Detection And Compensation System For Wound Rotor Synchronous Motor Based On Neural Networks	Simulated	Not Available	Current
Proposed Dataset	Custom	Real	Available	Voltage, Current and Vibration

Most current research and datasets concentrate on electrical or mechanical failures alone, neglecting the occurrence of mixed fault states that more accurately reflect real-world industrial situations. In practice, motors often encounter numerous simultaneous faults, such as a bearing failure and a phase imbalance, complicating diagnosis due to overlapping frequency components and nonlinear interactions between mechanical vibrations and electrical current signals. Identifying these concurrent defects is a significant challenge. A bearing defect may generate vibration signals that obscure the current distortion resulting from phase loss, or the reverse may occur. Our dataset rectifies this deficiency by intentionally introducing electrical failures (such as phase removal, operating at various load and starting of a motor without one phase) in a motor that already displays a mechanical bearing issue. This incorporates authentic coupled fault circumstances into the dataset, facilitating the creation and evaluation of more resilient diagnostic models capable of generalizing across various fault domains.

The paper aims to address the gap by providing a comprehensive data collection methodology that combines vibration analysis and electrical measurements to diagnose faults in three-phase induction motors. Through the acquisition of high-resolution data from sensors that track vibration, voltage, and current, this paper developed a comprehensive dataset that can be used to identify motor faults in both faulty and healthy motors operating in different conditions. The purpose of the experimental setup is to replicate real-world operational conditions, including mechanical misalignments, electrical imbalances, and phase loss. This creates a diverse dataset that helps with fault diagnosis and the creation of predictive models. The research methodology not only builds on the strengths of existing datasets but also enhances them by ensuring the real-time synchronization of sensor data, which is essential for precise cross-analysis. Moreover, the dataset is rigorously validated, ensuring accuracy and reliability for use in future research. The emphasis on data security and integrity also makes this dataset a robust resource for the wider research community, enabling the development of next-generation fault detection systems that can be applied in industrial settings. In the following sections, the detailed experimental setup, parameter selection, data collection process, and technical validation of the dataset are outlined.

Methods

In this section, the data collection strategy and analysis is presented. The Fig. 1 outlines the steps followed in the collection of the data, organized into the four key sub-section: Experimental Setup, Parameter Selection, Data Collection, Data Storage and Security and technical Validation.

Fig. 1.

Overview of the Data Collection Methodology.

Experimental setup

The goal of the experimental setup was to collect comprehensive data on the three phase induction motor’s operating conditions. A three-phase a 0.2 kW 4 pole squirrel cage induction motor powered by a universal AC supply was used in the setup²⁴. The setup also incorporated a Four-Quadrant Dynamometer/Power Supply, which was coupled to the motor enabling precise control of motor loading conditions. ADXL335²⁵, an analog accelerometer to record the vibrational data in real time while the motor is operating is used. The ADXL335 was mounted on the induction motor to accurately capture the vibrational data. An isolated sensor module²⁶ used for monitoring and measuring the voltage and current fluctuations. The voltage sensors were connected in parallel and the current sensors are connected in series between the three phase induction motor and the AC power supply for each individual phase. The data acquisition devices were connected to a central data logging unit via dSPACE using BNC connectors which enables real-time monitoring and synchronization of the accelerometer and electrical measurements. The motor is tested under no-load conditions and load conditions during the experiment to capture a variety of operational behaviors, simulating both faulty and normal operations. This setup as seen in Fig. 2 provides an extensive platform for gathering the data required to assess the motor’s performance and identify any faults.

Fig. 2.

Experimental Setup.

Parameter selection

Parameter selection is crucial in the data collection process. The three important measurements are the current, voltage and acceleration as seen in Table 2. The ADXL335 analog sensor utilizes a conventional 3-axis coordinate system to records the linear and angular movements of the motor (X, Y and Z) and are measured in g units. The ADXL335 has a sensitivity range of ±3 g in each of the axes. The ADXL335 was subjected to a proper calibration process, where the maximum and minimum g-values for each axis were measured and used to normalize the output. This vibrational information is crucial in identifying mechanical issues such as bearing faults or faults which are indicated by the abnormal vibrations. Voltage is obtained using the isolated sensor module, which is measured in volts (V), thereby monitoring the power supplied to the motor. Current measured in amperes (A) was captured using the current sensor connected to the isolated sensor module, which provided insights in the motor’s load conditions and power consumptions.

Table 2.

List of Sensors.

Sensor Name	Unit	Description	Data Format
Accelerometer	g	Measures the vibrations of all 3 axes	CSV
Voltage	V	Measures the Voltage	CSV
Current	A	Measures the current	CSV

The data collecting system’s sampling rate was customized using the dSPACE platform and configured at 50 kHz, ensuring high-resolution capturing of both steady-state and transient events. The 50 kHz sampling rate was chosen to ensure reliable detection of short-duration mechanical faults. This high sampling frequency was uniformly applied to measure the accelerometer, voltage, and current signals. Given that typical bearing fault frequencies and harmonics rarely exceed 10 kHz, the selected rate comfortably satisfies the Nyquist criterion and provides sufficient resolution for detailed vibration analysis, as well as detailed tracking of voltage and current fluctuations, providing a comprehensive view of the motor’s dynamic behavior and power characteristics.

Data collection session

The data collection process was carried out using two different induction motors: healthy motor and a faulty motor. The faulty motor used in our experiment had a bearing with an artificially induced outer ring fault. Specifically, multiple small holes were drilled into the outer ring of a standard deep groove ball bearing (SKF 6202-Z) to simulate localized defects. This model is widely used in small induction motors and allows for the calculation of fault characteristic frequencies (e.g., BPFO, BSF) based on known bearing dimensions. This damage represents real-world situations where bearing deterioration often origin at the outer race due to fatigue, contamination, or misalignment. The existence of these faults led to significant increases in vibration amplitudes. This mechanical fault configuration enabled us to monitor and document the impact of localized bearing damage on the motor’s performance under diverse operating situations.

Both motors were tested under multiple operating scenarios, including:

• Normal operational condition (three-phase supply fully connected)

• Phase removal during motor operation (disconnection of one phase during operation of Motor)

• Motor operation under a mechanical load of 0.4 Nm

• Motor operation under a mechanical load of 0.8 Nm

• Starting of a motor with one phase permanently disconnected from startup

While both “Phase Removal During Motor Operation” and “Starting of a Motor with One Phase Permanently Disconnected from Startup” involve phase loss, they differ significantly in their processes and effects on motor performance. In the former case, the motor first reaches steady-state under normal three-phase conditions, and phase loss introduces transient instability during operation. In contrast, in the latter case, the motor attempts to start with an incomplete phase configuration, leading to severe torque imbalance, startup difficulties, and elevated mechanical and electrical stress from the outset. Additionally, motors were operated under varying mechanical load conditions (0.4 Nm and 0.8 Nm). These variations induced changes in motor speeds and mechanical stress profiles, capturing a broader spectrum of motor behaviors and enhancing the robustness of fault diagnosis models. By incorporating multiple electrical and mechanical fault scenarios, the dataset was designed to replicate real-world operational conditions, enabling comprehensive analysis of simultaneous fault behaviors. The motor is operated at 280 V from the AC supply. Phase B was removed to simulate phase lose. To improve the reliability and accuracy of the results, the data collection process was repeated multiple times for each scenario in both motors. These Faults conditions are simulated to observe the motor behavior under electrical imbalances, mechanical misalignments and other potential issues. The dSPACE system records the data from the voltage, current, and accelerometer sensor in real time. Through system synchronization of all sensor data streams, precise cross-analysis of various parameters was made feasible. Signal plots are created during the data collection process to illustrate the sensor readings and to provide an overview of the motor’s performance as seen in Fig. 3. These plots assist in confirming the data’s accuracy as well as to certain that no abnormalities are missed during the session. In order to facilitate the early identification of possible faults, the dSPACE system is additionally set up to generate alerts if any sensor data deviates from expected values. The primary focus throughout the data collection process was always safety. Every step of the data collection process was carefully carried out while adhering to all applicable local laws and regulations. The data was collected for 20 seconds for each scenario.

Fig. 3.

dSPACE Application during data collection.

Data storage and security

A crucial component of this research is guaranteeing data security and storage. The acquired datasets are kept on a safe server with routine automated backups following each data collection session to guard against data loss or corruption. The integrity of the data is preserved because only authorized personnel may access the stored data via a secure login process. Encryption protocols are used during data transmission, especially when moving data between devices or storage locations, to safeguard the privacy and confidentiality of the data²⁷. Along with security precautions, data integrity and accuracy are frequently verified. All sensors received calibration checks to ensure accuracy, and post-processing filters out any noise or interference in the sensor signals. The dSPACE system maintains thorough logs of all operations associated with data acquisition, which are reviewed on a regular basis to verify that all sensors are in sync and that no data has been misplaced or corrupted. Using Google Cloud Storage, regular and systematic backups of the collected data were also carried out to guard against data loss and add an extra layer of protection against unexpected events.

Data Records

Structure

The collected data during the experiment was systematically recorded and saved in ten distinct CSV files and has been deposited on Figshare²⁸ (10.6084/m9.figshare.27216219) for public access. Every file is associated with a particular motor and captures the induction motor’s dynamic behavior in both healthy and faulty situations. Each file contains the same number of data points. The separation of data into distinct files facilitates better organization and clarity, enabling more effective analysis and comparison of various operational scenarios. These CSV files provide a comprehensive description of the motor’s performance under various test conditions by storing data logged in real-time from the voltage, current, and accelerometer sensors.

File structure

The data was organized into ten CSV files corresponding to the different operating conditions of the motor during the data collection sessions. These files are as follows:

• File 1: Healthy Motor – Normal Operation (No Load)²⁸

• File 2: Healthy Motor – Phase Removal During Operation (No Load)²⁸

• File 3: Healthy Motor – Operation Under 0.4 Nm Mechanical Load²⁸

• File 4: Healthy Motor – Operation Under 0.8 Nm Mechanical Load²⁸

• File 5: Healthy Motor – Running with One Phase Disconnected from Startup²⁸

• File 6: Faulty Motor – Normal Operation (No Load) ²⁸

• File 7: Faulty Motor – Phase Removal During Operation (No Load)²⁸

• File 8: Faulty Motor – Operation Under 0.4 Nm Mechanical Load²⁸

• File 9: Faulty Motor – Operation Under 0.8 Nm Mechanical Load²⁸

• File 10: Faulty Motor – Running with One Phase Disconnected from Startup²⁸

Thousands of data points captured at high frequencies are included in every CSV file, providing an accurate picture of the electrical and mechanical performance of the motor at any given time. The dSPACE system, which guaranteed consistent and dependable recordings from every sensor, enabled the comprehensive and coordinated data collection procedure.

Every CSV file has a consistent structure that makes it simple to navigate between them and guarantees that all pertinent data is recorded for every test case. The dataset enables focused analysis of particular problems, such as how phase removal affects the current and voltage or how mechanical vibrations differ between a healthy and a faulty motor, by segmenting the data into files based on motor health and operating conditions. This methodical arrangement makes it easier to identify particular issues and anomalies and offers a clear path for troubleshooting and motor diagnostics.

Description of fields in CSV

The following fields are present in each CSV file:

Voltage (V): This column records the voltage supplied to the motor at each time step. The voltage data helps in identifying any irregularities in the power supply, such as phase loss or voltage imbalances, particularly during fault conditions.

Current (A): This field records the current drawn by the motor in amperes²⁹. The current data is crucial for monitoring the motor’s load condition and detecting any electrical faults or irregularities in the motor’s performance. Changes in current readings can indicate issues such as electrical imbalances or phase removal.

Acceleration X (g): This column represents the acceleration data captured by the ADXL335 accelerometer along the X-axis. The data reflects the vibration and mechanical movement of the motor in one direction. Changes in these values are used to detect mechanical misalignment or faults³⁰.

Acceleration Y (g): This field captures the acceleration of the motor along the Y-axis. This data is useful in diagnosing any lateral vibrations or misalignments that may occur during motor operation³⁰.

Acceleration Z (g): The acceleration along the Z-axis is recorded in this column. The Z-axis data complements the X and Y axes and provides a complete 3D view of the motor’s mechanical behavior, helping to detect more complex mechanical issues like rotor imbalance or bearing faults³⁰.

Technical Validation

An extensive set of rigorous assessments was conducted as part of the technical validation process to guarantee the accuracy, relevance, and dependability of the data gathered from the motor fault experiments. First, calibration was done on the sensors used in the experimental setup, such as the voltage and current sensors, the ADXL335 accelerometer, and others, both before and after each data collection session. This rendered guaranteed that the sensors would continuously give precise readings for the duration of the experiment. During the calibration process, measurement drift and noise interference that might have affected the quality of the recorded data were reduced by comparing sensor readings with standard reference values to ensure their accuracy.

Throughout the data acquisition process, signal integrity was the main priority, and dSPACE was utilized to synchronize all sensor readings in real time. Accurate alignment of the electrical and mechanical measurements is essential for analyzing transient faults or anomalies in motor performance, and this real-time synchronization ensured that. While any misalignment between these datasets could lead to inaccurate fault diagnoses, high-fidelity time-series data was produced by precisely coordinating all sensor data streams through the use of dSPACE.

During post-processing, filtering techniques were used in addition to signal synchronization to remove any noise or interference that might have happened during data collection. As high-frequency noise can affect accelerometer data, it was especially crucial to apply low-pass filters. By taking this step, it was made sure that only pertinent vibrational data was saved, protecting the data integrity required to identify mechanical issues like bearing wear or rotor imbalances.

In order to make sure the data acquisition system could reliably record both steady-state and transient events, it was also put through stress testing under a variety of fault scenarios, such as simulated phase loss and mechanical misalignments.

Careful analysis of high-frequency accelerometer data sampled at 50 kHz proved sufficient to capture vibrational changes during malfunctioning operations, confirming the ability to detect short-duration anomalies. These fleeting occurrences were successfully identified by the system, proving its resilience in observing abrupt alterations in motor behavior. The dSPACE system’s automated checks were used to continuously monitor the data’s integrity. Real-time alerts were triggered during data collection in response to any significant deviations from expected sensor values. This allowed for prompt validation and, if needed, adjustments to the experimental setup. Version control was applied to all collected data to track changes and guarantee dataset consistency across multiple sessions. This proactive approach to validation ensured that no important data was missed or misreported.

The figures above illustrate the data captured by the accelerometer, current, and voltage sensors during various operational phases of both healthy and faulty induction motors, as recorded by the dSPACE system.

Figure 4 illustrates the performance of the healthy motor under normal conditions. Figure 4(A) shows accelerometer data across the X, Y, and Z axes, clearly identifying three phases: the motor’s off state, startup phase, and steady running phase. The increase in vibration amplitude during startup indicates the mechanical transition before stabilizing in normal operation. Figure 4(B) presents current readings for the three phases (I1, I2, I3), showing a sharp increase during startup, corresponding to the higher electrical demand, followed by stabilization. Figure 4(C) displays voltage data for the phases (V1, V2, V3), with a clear voltage ramp-up during startup and subsequent stabilization during regular operation.

Fig. 4.

Signal Plot of (A) Acceleration, (B) Current, (C) Voltage Sensor for a Healthy Motor Under Normal Operating Conditions.

Figure 5 illustrates the healthy motor’s behavior during phase removal under operation. Figure 5(A) shows a marked increase in vibration amplitude during phase removal, indicating mechanical instability, which diminishes as normal conditions are restored. Figure 5(B) shows significant fluctuations in current readings (I1, I2, I3) as the motor compensates for the missing phase. Figure 5(C) highlights the voltage imbalance, where V3 remains stable, but V1 and V2 drops sharply due to the phase loss.

Fig. 5.

Signal Plot of (A) Acceleration, (B) Current, (C) Voltage Sensor for a Healthy Motor During Phase Removal Under Operation.

Figure 6 examines the healthy motor running operating under a load of 0.4Nm. Figure 6(A) shows a significant increase in vibration amplitude, due to the addition of load. Figure 6(B) presents the current data, where I1, I2 and I2 presents a slight increase in current. Similarly, Fig. 6(C) reveals voltage data, with V1, V2, and V3 remaining stable with slight increase.

Fig. 6.

Signal Plot of (A) Acceleration, (B) Current, (C) Voltage Sensor for a Healthy Motor Operating Under a 0.4 Nm Load.

Figure 7 presents the healthy motor running operating under a load of 0.8Nm. Figure 7(A) shows a significant increase in vibration amplitude, compared to motor running at a load of 0.4Nm. Figure 7(B) presents the current data, where I1, I2, and I2 presents a slight increase in current from the previous load condition. Similarly, Fig. 7(C) reveals voltage data, with V1, V2 and V3 remaining stable with comparable increase.

Fig. 7.

Signal Plot of (A) Acceleration, (B) Current, (C) Voltage Sensor for a Healthy Motor Operating Under a 0.8 Nm Load.

Figure 8 examines the healthy motor running with one phase disconnected from startup. Figure 8(A) shows a significant increase in vibration amplitude, signaling severe mechanical instability that subsides as the motor powers down. Figure 8(B) presents the current data, where I1 and I3 show surges during startup, while I2 remains at zero, indicating no current flow in the missing phase. Similarly, Fig. 8(C) illustrates the voltage signals, where V3 exhibit a gradual increase during startup, while V2 and V1 reaches approximately half the magnitude of V3 throughout the period, reflecting the electrical imbalance from the missing phase.

Fig. 8.

Signal Plot of (A) Acceleration, (B) Current, (C) Voltage Sensor for a Healthy Motor Running with One Phase Disconnected from Startup.

Figure 9 captures the performance of the faulty motor under normal operational conditions. Figure 9(A) shows accelerometer data across the X, Y, and Z axes, clearly identifying three phases: the motor’s off state, startup phase, and steady running phase. The increase in vibration amplitude during startup indicates the mechanical transition before stabilizing in normal operation. Figure 9(B) presents current readings for the three phases (I1, I2, I3), showing a sharp increase during startup, corresponding to the higher electrical demand, followed by stabilization. Figure 9(C) displays voltage data for the phases (V1, V2, V3), with a clear voltage ramp-up during startup and subsequent stabilization during regular operation.

Fig. 9.

Signal Plot of (A) Acceleration, (B) Current, (C) Voltage Sensor for a Faulty Motor Under Normal Operating Conditions.

Figure 10 presents the faulty motor’s behavior during phase removal under operation. Figure 10(A) shows a marked increase in vibration amplitude during phase removal, indicating mechanical instability, which diminishes as normal conditions are restored. Figure 10(B) shows significant fluctuations in current readings (I1, I2, I3) as the motor compensates for the missing phase. Figure 10(C) highlights the voltage imbalance, where V3 remain stable, but V1 and V2 drops sharply due to the phase loss.

Fig. 10.

Signal Plot of (A) Acceleration, (B) Current, (C) Voltage Sensor for a Healthy Motor During Phase Removal Under Operation.

Figure 11 examines the faulty motor’s running operating under a load of 0.4Nm. Figure 11(A) shows a significant increase in vibration amplitude, due to the addition of load. Figure 11(B) presents the current data, where I1, I2 and I2 presents a slight increase in current. Similarly, Fig. 11(C) reveals voltage data, with V1, V2, and V3 remaining stable with slight increase.

Fig. 11.

Signal Plot of (A) Acceleration, (B) Current, (C) Voltage Sensor for a Faulty Motor Operating Under a 0.4 Nm Load.

Figure 12 presents the faulty motor’s running operating under a load of 0.8Nm. Figure 12(A) shows a significant increase in vibration amplitude, compared to motor running at a load of 0.4Nm. Figure 12(B) presents the current data, where I1, I2, and I2 presents a slight increase in current from the previous load condition. Similarly, Fig. 12(C) reveals voltage data, with V1, V2 and V3 remaining stable with comparable increase.

Fig. 12.

Signal Plot of (A) Acceleration, (B) Current, (C) Voltage Sensor for a Faulty Motor Operating Under a 0.8 Nm Load.

Figure 13 examines the healthy motor running with one phase disconnected from startup. Figure 13(A) shows a significant increase in vibration amplitude, signaling severe mechanical instability that subsides as the motor powers down. Figure 13(B) presents the current data, where I1 and I3 show surges during startup, while I2 remains at zero, indicating no current flow in the missing phase. Similarly, Fig. 13(C) illustrates the voltage signals, where V3 exhibit a gradual increase during startup, while V1 and V2 remains approximately half the magnitude of V3 throughout the period, reflecting the electrical imbalance from the missing phase.

Fig. 13.

Signal Plot of (A) Acceleration, (B) Current, (C) Voltage Sensor for a Faulty Motor Running with One Phase Disconnected from Startup.

Table 3 represents the speed measured for each motor under each scenario.

Table 3.

Operational Conditions and corresponding speeds of the Motor.

File No.	Condition Description	Motor Type	Speed (r/min)
File 1	Normal Operation (No Load)	Healthy	1428
File 2	Phase Removal During Operation (No Load)	Healthy	1366
File 3	Operation Under 0.4 Nm Load	Healthy	1389
File 4	Operation Under 0.8 Nm Load	Healthy	1260
File 5	Running with One Phase Disconnected from Startup	Healthy	0
File 6	Normal Operation (No Load)	Faulty	1311
File 7	Phase Removal During Operation (No Load)	Faulty	1254
File 8	Operation Under 0.4 Nm Load	Faulty	1279
File 9	Operation Under 0.8 Nm Load	Faulty	1101
File 10	Running with One Phase Disconnected from Startup	Faulty	0

The ten distinct data files were merged to create a single comprehensive dataset, which was used to train a machine learning classification model aimed at identifying various motor faults, demonstrating the dataset’s applicability in fault detection²⁸. The dataset was categorized into thirteen distinct fault types, as detailed in Table 4 depending on each scenario where both the healthy and faulty motor was operated.

Table 4.

Fault Classification Category.

CLASSIFICATION	CATEGORY
Motor Off (No Operation State)	1
Faulty Motor During Startup	2
Faulty Motor in Normal Operation	3
Faulty Motor – Phase Removed During Running	4
Faulty Motor – No Phase From Startup	5
Healthy Motor During Startup	6
Healthy Motor in Normal Operation	7
Healthy Motor – Phase Removed During Running	8
Healthy Motor – No Phase From Startup	9
Faulty Motor Under 0.4 Nm Load	10
Faulty Motor Under 0.8 Nm Load	11
Healthy Motor Under 0.4 Nm Load	12
Healthy Motor Under 0.8 Nm Load	13

To classify the dataset, a sliding window segmentation strategy was employed to extract fixed-length, labeled samples from ten raw data files containing multichannel time-series recordings of both healthy and faulty motor conditions. Each file captured various operational states, including motor starting, normal operation, and fault conditions such as phase removal. To prepare the data for supervised learning, a sliding window with a length of 1000 samples and a step size of 500 samples was applied across each recording³¹. This method ensured that overlapping windows were generated, preserving important temporal information while increasing the number of training instances, especially for underrepresented classes. Each window was labeled according to the motor condition present during its span, resulting in a well-structured dataset comprising 19,982 samples. Each sample represents 1000 consecutive time steps with 10 sensor features per step and was subsequently flattened into a 1D vector of 10,000 elements to be compatible with traditional machine learning models. In this representation, columns 0–9 correspond to the 10 features at time step 0, columns 10–19 represent time step 1, and this pattern continues up to columns 8990–8999, which represent the features at time step 999. The final column in each row holds the class label indicating the specific motor condition. This structured format facilitates effective training and evaluation of classification models, enabling accurate detection of various motor faults based on temporal patterns in electrical and mechanical sensor data.

Random Forrest classifier³² was used for training and testing the comprehensive dataset. The model performed exceptionally well, with an overall accuracy of 99.82%, High precision, recall, and F1-scores³³ as seen in Table 5. The Receiver Operating Characteristic (ROC)³⁴ and Confusion Matrix³⁵ as seen in Figs. 14, 15 indicates a well-tuned model, effective at distinguishing between different classes. This high degree of precision and accuracy demonstrates how well the model and dataset work together to identify and categorize different types of motor faults, providing a reliable option for predictive maintenance and real-time fault diagnosis. As demonstrated by the PRISMA Diagram in Fig. 16, we were unable to locate any publicly available dataset that was suitable for validating our custom dataset, despite conducting exhaustive queries across these reputable platforms. These findings emphasize the distinctive contribution of our research, emphasizing the necessity of acquiring additional datasets in the field to facilitate further validation and research. In the PRISMA diagram, the variable n denotes the number of records identified at each phase of the dataset selection process. The top box displays the records obtained from prominent academic databases like IEEE Xplore, ScienceDirect, MDPI, and Springer. The bottom box contains a higher number of n since it incorporates both the original database records and supplementary entries obtained via manual searches, citation chaining, and dataset repositories like Kaggle, IEEE DataPort, and institutional archives. This increase ensures a more comprehensive and inclusive review by incorporating relevant but non-indexed or less visible sources.

Table 5.

Performance of the Machine Learning Model.

Accuracy	Precision	Recall	F1 Score
99.82	0.9982	0.9982	0.9983

Fig. 14.

Receiver Operating Characteristic (ROC) curve.

Fig. 15.

Confusion Matrix.

Fig. 16.

PRISMA DIAGRAM.

Author contributions

K.T., A.K., and W.R. initiated the idea. K.T. wrote the initial draft and did the data analysis. K.T., A.R., A.K. and W.R. collected data. Other authors helped in manuscript preparation and provided critical revisions. All authors have read and approved the final manuscript.

Code availability

No custom code was used for the curation of the dataset in this study.

Competing interests

The authors declare no competing interests.