System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated storage and retrieval systems

Baek, Sujeong

doi:10.1007/s00170-021-06652-z

Cited by 14 publications

(15 citation statements)

References 32 publications

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“…Although the failure rate of wind turbine gearbox is relatively low, it can lead to the longest downtime and the highest maintenance costs. erefore, an e ective method is needed to monitor its operating status and send out early warning information before failure [1]. Vibration monitoring is an e ective method to monitor the condition of wind turbine gearbox.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Wind Turbine Gearbox Based on Multisensor Data Fusion

Zhang

Zhou

2022

Journal of Control Science and Engineering

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In order to improve the operation efficiency of wind turbine gearbox and reduce the operation and maintenance cost of wind farm, a fault diagnosis system for wind turbine gearbox based on multisensor data fusion was proposed. First, the different time-domain statistical characteristic parameters of the original vibration signal were calculated, and the information fusion of the feature level and the data level was carried out by means of parallel superposition to obtain the fused data set. Second, a fault classification and recognition model based on GMO-KELM was established by using the fusion data set. Finally, the proposed method was used to monitor the status of the measured data of the gearbox on the vibration test bed of rotating machinery. The experimental results showed that the average training accuracy and the average test accuracy of GMO-KELM method were 100% and 95.58%, respectively, which were much higher than those of other methods. Through experiments and analyses, it was shown that the proposed method was effective and feasible. Compared with other similar methods, the proposed method had the best classification performance.

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Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Wind Turbine Gearbox Based on Multisensor Data Fusion

Zhang

Zhou

2022

Journal of Control Science and Engineering

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“…Fault detection and diagnosis (FDD) approaches based on traditional statistical process control charts as well as advanced data mining techniques have been employed in various manufacturing applications [5,[29][30][31][32]. As summarized in Table 1, statistical chart-based FDD approaches provide a satisfactory detection performance when sensor signals are only collected during the normal operation of a system, and the corresponding measurements are concentrated in one or several clusters.…”

Section: Literature Review On Fault Detection Methodsmentioning

confidence: 99%

“…For effective manufacturing operations, corrective recovery action(s) should be manually performed by operators to prevent the detected faults. For example, for the effective operation of an automated storage and retrieval system (ASRS), Internetof-things-based controllers and sensors were installed [31]. Depending on the current system status determined by analyzing real-time vibration signals, the controller changes the relevant process parameter (i.e., motor speed in transporters) and, consequently, the ASRS can automatically maintain a failure-free status.…”

Section: Literature Review On Fault Detection Methodsmentioning

confidence: 99%

“…Predictive process adjustment is a representative approach in predictive maintenance. The concept of this approach is to automatically perform appropriate feedback actions, which are determined by detecting and diagnosing the current system status or predicting an upcoming fault status through real-time monitoring [31]. Therefore, in this paper, early fault detection and recovery actions are proposed in order to conduct predictive process adjustment in real time, as illustrated in Figure 4.…”

Section: Methodsmentioning

confidence: 99%

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Predictive Process Adjustment by Detecting System Status of Vacuum Gripper in Real Time during Pick-Up Operations

Baek

Kim

2021

Processes

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In manufacturing systems, pick-up operations by vacuum grippers may fail owing to manufacturing errors in an object’s surface that are within the allowable tolerance limits. In such situations, manual interference is required to resume system operation, which results in considerable loss of time as well as economic losses. Although vacuum grippers have many advantages and are widely used in the industry, it is highly difficult to directly monitor the current machine status and provide appropriate recovery feedback for stable operation. Therefore, this paper proposes a method to detect the success or failure of a suction operation in advance by analyzing the amount of outlet air pressure in the Venturi line. This was achieved by installing an air pressure sensor on the Venturi line to predict whether the current suction action will be successful. Through empirical experiments, it was found that downward movements in the z-axis of the vacuum gripper can easily rectify a faulty gripper suction operation. Real-time monitoring results verified that predictive process adjustment of the pick-up operation can be performed by modifying the z-position of the vacuum gripper.

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“…However, with the increasing electronic, automated, and connected control systems, the functions and performance requirements undertaken by real-time systems are becoming more and more demanding. At the same time, the type, number, and a load of tasks in the system have increased dramatically, which poses a great challenge to the time verification of real-time systems [7]. Therefore, it is of great significance to research schedulability analysis and performance optimization of complex real-time task systems.…”

Section: Introductionmentioning

confidence: 99%

High-Capacity Real-Time Face Retrieval Recognition Algorithm Based on Task Scheduling Model for the Treatment Area of Hospital

Xia

2021

Advances in Mathematical Physics

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This paper presents an in-depth study of face detection, face feature extraction, and face classification from three important components of a high-capacity face recognition system for the treatment area of hospital and a study of a high-capacity real-time face retrieval and recognition algorithm for the treatment area of hospital based on a task scheduling model. Considering the real-time nature of our system, our face feature extraction network is modeled by DeepID, and the network is slightly improved by introducing a central loss verification signal to train a DeepID-like network model using central loss and use it to extract face features. To further investigate and optimize the schedulability analysis problem of the directed graph real-time task model, this paper proposes a rigorous and approximate response time analysis method for the directed graph real-time task model with an arbitrary time frame. Based on the theoretical results of the greatly additive algebra, it is shown that the coherent qualifying function is linearly periodic, i.e., the function can be represented by a finite nonperiodic part and an infinitely repeated periodic part, thus calculating the coherent qualifying function independent of the magnitude of the interval time. The algorithm for high-capacity real-time face retrieval and recognition in the treatment area of hospital based on the task scheduling model is further investigated, and a face database is established by using the PCA dimensionality reduction technique. Based on the internal architecture of the processor, image preprocessing and IP core packaging are implemented, and the hardware engineering of the high-capacity real-time face recognition system for hospital visits is built using the IP-based design concept. The performance tests of the face detection model and feature extraction network show that the face detection model has a significant reduction in false-positive rate, better fitting of border regression, and improved time performance. The face feature extraction network has no overfitting, and the features are highly discriminative with small feature extraction time consumption. The high-capacity real-time face recognition system for the treatment area of hospital combined with the optimized directed graph task scheduling model can approach 25 fps, which meets the real-time requirements, and the face recognition rate surpasses that of real people. It realizes the intelligence, self-help, and autonomy of medical services and satisfies the medical needs of users in all aspects.

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System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated storage and retrieval systems

Cited by 14 publications

References 32 publications

Fault Diagnosis of Wind Turbine Gearbox Based on Multisensor Data Fusion

Fault Diagnosis of Wind Turbine Gearbox Based on Multisensor Data Fusion

Predictive Process Adjustment by Detecting System Status of Vacuum Gripper in Real Time during Pick-Up Operations

High-Capacity Real-Time Face Retrieval Recognition Algorithm Based on Task Scheduling Model for the Treatment Area of Hospital

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