Industrial information integration engineering (IIIE) is a set of foundational concepts and techniques that facilitate the industrial information integration process. In recent years, many applicat...

A Survey on Industrial Information Integration 2016–2019

Artificial intelligence (AI)-based rotor fault diagnosis (RFD) poses a variety of challenges to the prognostics and health management (PHM) of the Industry 4.0 revolution. Rotor faults have drawn more attention from the AI research community in terms of utilizing fault-specific characteristics in its feature engineering, compared to any other rotating machinery faults. While the rotor faults, specifically structural rotor faults (SRF), have proven to be the root cause of most of the rotating machinery issues, the research in this field largely revolves around bearing and gear faults. Within this scenario, this paper is the first of its kind to attempt to review and define the role of AI in RFD and provides an all-encompassing review of rotor faults for the researchers and academics. In addition, this study is unique in three ways: (i) it emphasizes the use of fault-specific characteristic features with AI, (ii) it is grounded in fault-wise analysis rather than component-wise analysis with appropriate fault categorization, and (iii) it portrays the current research and analysis in accordance with different phases of an AI-based RFD framework. Finally, the section on future research directions is aimed at bridging the gap between a laboratory-based solution and a real-world industrial solution for RFD.

Role of artificial intelligence in rotor fault diagnosis: a comprehensive review

Multiphase drives offer enhanced fault-tolerant capabilities compared with conventional three-phase ones. Their phase redundancy makes them able to continue running in the event of faults (e.g., open/short-circuits) in certain phases. Moreover, their greater number of degrees of freedom permits improving diagnosis and performance, not only under faults affecting individual phases, but also under those affecting the machine/drive as a whole. That is the case of failures in the dc link, resolver/encoder, control unit, cooling system, etc. Accordingly, multiphase drives are becoming remarkable contenders for applications where high reliability is required, such as electric vehicles and standalone/off-shore generation. Actually, the literature on the subject has grown exponentially in recent years. Various review papers have been published, but none of them currently cover the state-of-the-art in a comprehensive and up-to-date fashion. This two-part paper presents an overview concerning fault tolerance in multiphase drives. Hundreds of citations are classified and critically discussed. Although the emphasis is put on fault tolerance, fault detection/diagnosis is also considered to some extent, because of its importance in fault-tolerant drives. The most important recent advances, emerging trends and open challenges are also identified. Part 1 provides a comprehensive survey considering numerous kinds of faults, whereas Part 2 is focused on phase/switch open-circuit failures.

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A Comprehensive Survey on Fault Tolerance in Multiphase AC Drives, Part 1: General Overview Considering Multiple Fault Types

Artificial intelligence (AI)-based rotating machinery fault diagnosis has extreme importance in the industrial automation and control systems since rotating machinery constitutes approximately 40% of the overall machinery in the industry. However, the majority of AI-based solutions in rotor faults diagnosis are in an experimental stage due to: 1) inadequate and unrealistic faulty data; 2) lack of opportunities to utilize domain-specific fault features; and 3) limitations in employing deep learning and advanced learning strategies. Moreover, structural rotor fault (SRF) is one of the critical but least addressed faults in rotor faults diagnosis even though it is the root cause of the majority of rotating machinery issues. Hence, we develop an SRF diagnosis framework, which addresses the issues of industrial data acquisition by creating a subsampled data set incorporating distinctive frequency components (DFC). The data scarcity and imbalance problems are handled through an augmentation method using soft-dynamic time warping (soft-DTW), enhanced by fault information content (FIC)-based weighing scheme. At the fault classification phase, we proposed an early classification (EC) approach for SRF that predicts the faults with an acceptable tradeoff between earliness and accuracy. In this line, first, a sequential deep learning classifier is developed by considering accuracy only as an objective. Then, early decision policy is defined by taking accuracy and earliness into account. The model demonstrated exceptional performance over state-of-the-art methods in SRF diagnosis and achieved 99.5% accuracy with 14% earliness on the Meggitt testbed data set and 98.32% accuracy with 55.68% earliness on the MaFaulDa data set.

An Early Classification Approach for Improving Structural Rotor Fault Diagnosis

Rotating machinery (RM) fault diagnosis based on artificial intelligence (AI) is an esteemed industrial IoT application and an inevitable constituent of the Industry 4.0 revolution. Notably, the existing research in this area exhibits the following limitations: i) the time-series characterization or system dynamics considerations of vibration input are not strictly followed in the diagnostic procedure, ii) fault-specific component usage is not encouraged in existing decision-making strategies, and iii) single classifier based decision-making commonly compromises in utilizing all features of input data. Structural rotor fault (SRF) is the most vital but least attended defect, which is the root cause of most RM issues. We develop a framework that uses two parallel decision-making strategies, i.e., the convolutional neural network (CNN) with fuzzy recurrence plot (FRP) as input and a long short-term memory (LSTM) network taking distinctive frequency components (DFC) stream as input. Moreover, a DFC-based ranking and image combining scheme is proposed to select the most informative sensor signals from the segmented raw vibration signals and generate a 3D-FRP, and 2D flattened FRP (F-FRP). These multi-sensor FRPs characterize the system dynamics and preserve the time-series properties of the vibration input. The DFC stream simultaneously catered to LSTM facilitates symptom parameters-based decision-making. The parallel decision scores are fused by fuzzy integral (FI) based fusion depending on the confidence of the sources. The model performs exceptionally well over the state-of-the-art methods and achieves around 99.0% accuracy with the Meggitt testbed dataset and the MaFaulDa dataset.

Improved Structural Rotor Fault Diagnosis Using Multi-Sensor Fuzzy Recurrence Plots and Classifier Fusion

Nowadays, mechanical fault detection techniques based on signals from motor drive are becoming popular. However, in electromechanical system with flexible drive-train, mechanical fault signature reflected in motor drive is suppressed by the transmission path. This signal attenuation reduces the effectiveness of fault detection. Therefore, research on the law of fault signature transmission from load to motor is necessary. In this article, a two-mass electromechanical system with a long shaft is concerned. First, the transmission path from load torque to electromagnetic torque is analyzed, and the concept of torque transmission efficiency is proposed. Then, the frequency characteristic method and transfer function method are presented to establish the signal transmission model. Finally, the model-based signal enhancement method is proposed to compensate the attenuation. Simulation and experimental results show that the proposed method can enhance the quality of fault signature transmission and improve the performance of fault classification.

Signal Enhancement Method for Mechanical Fault Diagnosis in Flexible Drive-Train

The main contribution of this paper is the strategy to use motor drive system as multivariable sensor, together with multi data fusion method for health evaluation. In the diagnosis of mechatronics, external sensors cause problems in signal acquisition stage. To overcome these problems, signals from the control process of motor drive system are directly collected to achieve multivariable self-sensing. Moreover, data fusion methods are proposed to convert massive data into RGB channel false color image and position-based polar image to carry comprehensive information. The CNN model is used to classify various working conditions. Quantitative simulation and experiment on a ball screw driven by permanent magnet synchronous machines (PMSM) are conducted to validate the proposed method. The results show that images with comprehensive information perform better in the diagnosis of mechatronics.

Data Fusion Methods for Convolutional Neural Network Based on Self-Sensing Motor Drive System

The invention discloses a linear motion system health monitoring method based on spatial domain information, and belongs to the field of electromechanical system health state monitoring The method comprises the steps: measuring a spatial position signal of a linear motion system, and an electric signal for dragging an AC motor of the linear motion system; converting the electric signal based on the time domain into a space domain; calculating a local characteristic index of the local travel interval and a global characteristic index of the whole motion travel; judging whether the local feature indexes and the global feature indexes exceed corresponding set thresholds or not; if any index exceeds a set threshold value, determining that the linear motion system is in an abnormal state at the corresponding spatial position; and if not, determining that the motion travels of the linear motion system are all in a healthy state According to the method, the measurement information based onthe time domain is converted into the space domain, the health state of the linear motion system on the whole motion stroke can be comprehensively and accurately reflected, the position where the abnormal state occurs is indicated, and the method has great application value for linear motion system full-stroke monitoring

Linear motion system health monitoring method based on spatial domain information

The invention discloses an online acquisition method for input active power and reactive power of an induction motor, belonging to the field of motor measurement and control. Through the manner of online calculation, the input power information of the induction motor can be better obtained in real time, so a basis is provided for operation monitoring and fault diagnosis extension research of an induction motor system. The method is implemented in a system through a software module, and no extra device needs to be added in the motor driving system, so the cost of the system is not increased. The average value of the reconstructed voltage of a current period and the reconstructed voltage of a previous switching period is used for replacing the output voltage of an inverter at an initial moment of the current period, so the phases of measured current and the reconstructed voltage are aligned; the input power of the induction motor is calculated once in each current loop period, so the real-time performance of power acquisition is improved, and updating time is short. The influence of a nonlinear part in the inverter is considered in the reconstruction of motor end voltage, so the accuracy of the reconstruction voltage is improved, and the accuracy of power estimation is further improved.

Online acquisition method for input active power and reactive power of induction motor

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Papers

Signal Enhancement Method for Mechanical Fault Diagnosis in Flexible Drive-Train

Data Fusion Methods for Convolutional Neural Network Based on Self-Sensing Motor Drive System

Linear motion system health monitoring method based on spatial domain information

Online acquisition method for input active power and reactive power of induction motor