This paper deals with the early detection of fault conditions in induction motors using a combined model- and machine-learning-based approach with flexible adaptation to individual motors. The method is based on analytical modeling in the form of a multiple coupled circuit model and a feedforward neural network. In addition, the differential evolution algorithm independently identifies the parameters of the motor for the multiple coupled circuit model based on easily obtained measurement data from a healthy state. With the identified parameters, the multiple coupled circuit model is used to perform dynamic simulations of the various fault cases of the specific induction motor. The simulation data set of the stator currents is used to train the neural network for classification of different stator, rotor, mechanical, and voltage supply faults. Finally, the combined method is successfully validated with measured data of faults in an induction motor, proving the transferability of the simulation-trained neural network to a real environment. Neglecting bearing faults, the fault cases from the validation data are classified with an accuracy of 94.81%.

In many areas of mechanical systems, condition monitoring is becoming an increasingly important discipline. Condition monitoring systems are used in many cases to monitor systems and components with sensor data in combination with machine learning algorithms. Small sample size and an unbalanced sample distribution are the main problems in implementing machine learning models for condition monitoring and fault classification. Generative Adversarial Networks (GAN) have proven to be a suitable approach for data augmentation to overcome both insufficient data per condition class and an unbalanced amount of data. A substantial amount of literature exists on the topic of synthetic data generation, which demonstrates a multitude of disparate approaches and use cases. Depending on the respective use cases, various pre-processing methods, network architectures and evaluation methods used are analyzed. In the scope of a systematic literature research the used methods and architectures are researched and visualized. In this paper, the different pre-processing methods for data augmentation, the different network architectures as well as the methods for evaluating the synthetically generated data in comparison to the real data are evaluated. Based on the literature review, an implementation approach for GAN is presented.

This paper addresses the further development and optimization of a modeling- and machine learning-based framework for early fault detection and diagnosis in induction motors. The goal behind the multi-level framework is to provide a pragmatic and practical approach for the autonomous monitoring of electrical machines in various industrial applications. The main contributions of this paper include the elimination of a fingerprint measurement in the processing of the framework and the development of a generalized model for fault detection and diagnosis. These aspects allow the training of neural networks with a simulated data set before even knowing the specific induction motor to be monitored. The pre-trained feed-forward neural networks enable the detection of several electrical and mechanical faults in a real induction motor with an overall accuracy of 99.56%. Another main contribution is the extension of the methodology to a larger operating range. As a result, various faults in a real induction motor can be detected under different load conditions with accuracies of over 92%. As a further part of the paper, a concept for a prototype is presented, which enables the autonomous and practice-friendly application of the framework.

Monitoring and diagnosis of fault cases for squirrel cage induction motors can be implemented using the multiple coupled circuit model. However, the identification of the associated model parameters for a specific machine is problematic. Up to now, the main options are measurement and test procedures or the use of finite element method analyses. However, these approaches are very costly and not suitable for use in an industrial application. The purpose of this paper is a practical parameter identification based on optimization methods and a comparison of different algorithms for this task. Population-based metaheuristics are used to determine the parameters for the multiple coupled circuit model. For this purpose, a search space for the required parameters is defined without an elaborate analytical approach. Subsequently, a genetic algorithm, the differential evolution algorithm and particle swarm optimization are tested and compared. The algorithms use the weighted mean squared error (MSE) between the real measured data of stator currents as well as speed and the simulation results of the model as a fitness function. The results of the parameter identification show that the applied methodology generally works and all three optimization algorithms fulfill the task. The differential evolution algorithm performs best, with a weighted MSE of 2.62, the lowest error after 1,000 simulations. In addition, this algorithm achieves the lowest overall error of all algorithms after only 740 simulations. The determined parameters do not completely match the parameters of the real machine, but still result in a very good reproduction of the dynamic behavior of the induction motor with squirrel cage. The value of the presented method lies in the application of condition-based maintenance of electric drives in the industry, which is performed based on the multiple coupled circuit model. With a parameterized model, various healthy as well as faulty states can be calculated and thus, in the future, monitoring and diagnosis of faults of the respective motor can be performed. Essential for this, however, are the parameters adapted to the respective machine. With the described method, an automated parameter identification can be realized without great effort as a basis for an intelligent and condition-oriented maintenance.

This paper presents an enhanced methodology for parameter diagrams (P-Diagrams) designed to support system analysis and sensor integration in condition monitoring. Building on the foundations of the AIAG & VDA and Ford FMEA Handbooks, the proposed approach extends the classical structure by introducing sensor-relevant variables, including SI units, expected measurement ranges, and sensor assignments. It also refines the classification of outputs into intended, unintended, and deviating categories, and provides a structured representation of control and noise factors. The methodology was validated using a screw compressor as a representative system. The case study illustrates how the enhanced P‑Diagram facilitates a detailed functional analysis, supports systematic sensor selection, and improves traceability in documenting system interactions. The results demonstrate the potential of the enhanced P‑Diagram as a practical and scalable modelling tool for condition monitoring. Future research may explore its application in other industrial domains and investigate the use of artificial intelligence and machine learning for automated diagram generation and refinement.