Comparison of PMSMs Motor Current Signature Analysis and Motor Torque Analysis Under Transient Conditions

Bonci, Andrea; Indri, Marina; Kermenov, Renat; Longhi, Sauro; Nabissi, Giacomo

doi:10.1109/indin45523.2021.9557553

Cited by 4 publications

(6 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Differently, to address and detect anomalies, it only used torque information because the authors demonstrated in previous work [ 8 , 26 ] that the torque signal contains strong faulty features related to the motor and the kinematic chain attached to the shaft.…”

Section: Methodsmentioning

confidence: 99%

Anomaly Detection and Concept Drift Adaptation for Dynamic Systems: A General Method with Practical Implementation Using an Industrial Collaborative Robot

Kermenov

Nabissi

Longhi

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

Industrial collaborative robots (cobots) are known for their ability to operate in dynamic environments to perform many different tasks (since they can be easily reprogrammed). Due to their features, they are largely used in flexible manufacturing processes. Since fault diagnosis methods are generally applied to systems where the working conditions are bounded, problems arise when defining condition monitoring architecture, in terms of setting absolute criteria for fault analysis and interpreting the meanings of detected values since working conditions may vary. The same cobot can be easily programmed to accomplish more than three or four tasks in a single working day. The extreme versatility of their use complicates the definition of strategies for detecting abnormal behavior. This is because any variation in working conditions can result in a different distribution of the acquired data stream. This phenomenon can be viewed as concept drift (CD). CD is defined as the change in data distribution that occurs in dynamically changing and nonstationary systems. Therefore, in this work, we propose an unsupervised anomaly detection (UAD) method that is capable of operating under CD. This solution aims to identify data changes coming from different working conditions (the concept drift) or a system degradation (failure) and, at the same time, can distinguish between the two cases. Additionally, once a concept drift is detected, the model can be adapted to the new conditions, thereby avoiding misinterpretation of the data. This paper concludes with a proof of concept (POC) that tests the proposed method on an industrial collaborative robot.

show abstract

Section: Methodsmentioning

confidence: 99%

Anomaly Detection and Concept Drift Adaptation for Dynamic Systems: A General Method with Practical Implementation Using an Industrial Collaborative Robot

Kermenov

Nabissi

Longhi

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Based on these previous work considerations, our approach will be based on Motor Torque Analysis (MTA), since in the authors' experience, torque analysis also provided interesting results regarding the diagnosis under non-stationary conditions (i.e., varying speed and load conditions) [16]. In fact, in [17], we already pointed out the possibility of overcoming limits related to bearing fault diagnosis under non-stationary conditions using MTA instead of MCSA. Furthermore, in [18], a review regarding the detection and diagnosis methodologies for gearboxes, it is demonstrated that the most useful signals for diagnosis are currents, torques, or vibrations.…”

Section: Related Workmentioning

confidence: 99%

ROS-Based Condition Monitoring Architecture Enabling Automatic Faults Detection in Industrial Collaborative Robots

2022

Self Cite

View full text Add to dashboard Cite

The Condition Monitoring (CM) of industrial collaborative robots (cobots) has the potential to decrease downtimes in highly automated production systems. However, in such complex systems, defining a strategy for effective CM and automatically detecting failures is not straightforward. In this paper, common issues related to the application of CM to collaborative manipulators are first introduced, discussed, and then, a solution based on the Robot Operating System (ROS) is proposed. The content of this document is highly oriented towards applied research and the novelty of this work mainly lies in the proposed CM architecture, while the methodology chosen to assess the manipulator’s health is based on previous research content. The CM architecture developed and the relative strategy used to process data are useful for the definition of algorithms for the automatic detection of failures. The approach is based on data labeling and indexing and aims to extract comparable data units to easily detect possible failure. The end of this paper is provided with a proof of concept (PoC) applied to an industrial collaborative manipulator where the proposed CM strategy has been implemented and tested in a real application scenario. Finally, it is shown how the proposed methodology enables the possibility of defining standard Health Indicators (HIs) to detect joint anomalies using torque information even under a highly dynamic and non-stationary environmental conditions.

show abstract

“…A single-wheel model is also defined in Figure 3. The commonly used Magic Tire formula [15] that takes into account the long drive is [28].…”

Section: -Dof Modelmentioning

confidence: 99%

“…Zhang et al proposed a fault-tolerant control method for steer-by-wire (SBW) based on the Kalman filtering technique to mitigate the adverse effects of steering angle sensor faults [14]. Bonci et al investigated the application of a load torque observer based on the extended Kalman filter in diagnosing electric drives operating under non-stationary conditions [15]. The data-driven method are also a standard method for fault diagnosis.…”

Section: Introductorymentioning

confidence: 99%

Back Propagation Neural Network-Based Fault Diagnosis and Fault Tolerant Control of Distributed Drive Electric Vehicles Based on Sliding Mode Control-Based Direct Yaw Moment Control

Sun,

Wong,

Wang

2023

Vehicles

View full text Add to dashboard Cite

Distributed-drive vehicles utilize independent drive motors on the four-wheel hubs. The working conditions of the wheel-hub motors are so harsh that the motors are prone to failing under different driving conditions. This study addresses the impact of drive motor faults on vehicle performance, particularly on slippery roads where sudden faults can lead to accidents. A fault-tolerant control system integrating motor fault diagnosis and a direct yaw moment control (DYC) based fault-tolerant controller are proposed to ensure the stability of the vehicle during various motor faults. Due to the difficulty of identifying the parameters of the popular permanent magnet synchronous wheel hub motors (PMSMs), the system employs a model-free backpropagation neural network (BPNN)-based fault detector. Turn-to-turn short circuits, open-phase faults, and diamagnetic faults are considered in this research. The fault detector is trained offline and utilizes rotor speed and phase currents for online fault detection. The system assigns the torque outputs from both healthy and faulted motors based on fault categories using sliding mode control (SMC)-based DYC. Simulations with four-wheel electric vehicle models demonstrate the accuracy of the fault detector and the effectiveness of the fault-tolerant controller. The proposed system is prospective and has potential for the development of distributed electric vehicles.

show abstract

Comparison of PMSMs Motor Current Signature Analysis and Motor Torque Analysis Under Transient Conditions

Cited by 4 publications

References 16 publications

Anomaly Detection and Concept Drift Adaptation for Dynamic Systems: A General Method with Practical Implementation Using an Industrial Collaborative Robot

Anomaly Detection and Concept Drift Adaptation for Dynamic Systems: A General Method with Practical Implementation Using an Industrial Collaborative Robot

ROS-Based Condition Monitoring Architecture Enabling Automatic Faults Detection in Industrial Collaborative Robots

Back Propagation Neural Network-Based Fault Diagnosis and Fault Tolerant Control of Distributed Drive Electric Vehicles Based on Sliding Mode Control-Based Direct Yaw Moment Control

Contact Info

Product

Resources

About