Bearing
Assembly Line
Identifying Bearing Faults Through Vibration Analysis
igus fixed flange bearings become intelligent
The new fixed flange bearings are currently in the prototype phase. However, they have already convinced one pilot customer: the Société National des Chemins de Fer Luxembourgeois (CFL). Luxembourg’s national railway company operates a 200-metre washing system that cleans local and long-distance trains every day. In the past, gearbox malfunctions on the washing trolley repeatedly led to costly system failures. The operator therefore abandoned lubricated standard ball bearings and instead used the networked spherical balls from igus in the UC bearing housings. The advantage is that the spherical balls made of high-performance plastics are corrosion-free, resist chemicals and enable low-friction and maintenance-free dry operation thanks to incorporated solid lubricants.
🧠📹 What Sets Toshiba’s Ceramic Balls Apart? The AI Quality Inspection System
Bearings cannot be easily replaced once a vehicle is assembled. In the U.S., bearings used in EVs are expected to be of high enough quality to withstand long distances. One issue that can occur with EVs, however, is the “electric corrosion” of the bearings that mount the various vital parts of the vehicle onto the motor—a serious issue, as it can lead to the breakdown of the vehicle. High-performance bearings would drive the widespread use of EVs, and contribute to the push towards carbon neutrality. The electrical corrosion phenomenon had hampered these efforts, but not anymore—therein lies the beauty of Toshiba’s ceramic balls.
“Our ceramic balls go through slight changes about every year and a half due to changes in material and other factors. To keep up the accuracy of the quality inspections, we have to continually update the AI system itself. The MLOps system automates that process,” says Kobatake.
“We’ve been able to dramatically reduce the time spent on these inspections. Ceramic balls are expensive compared to their metal counterparts. They have so many different strengths, and yet they haven’t been able to replace the metal ones precisely because of this particular issue. If we’re able to reduce the cost through AI quality inspection, we’ll be able to lower the price of the products themselves,” says Yamada.
A new intelligent fault diagnosis framework for rotating machinery based on deep transfer reinforcement learning
The advancement of artificial intelligence algorithms has gained growing interest in identifying the fault types in rotary machines, which is a high-efficiency but not a human-like module. Hence, in order to build a human-like fault identification module that could learn knowledge from the environment, in this paper, a deep reinforcement learning framework is proposed to provide an end-to-end training mode and a human-like learning process based on an improved Double Deep Q Network. In addition, to improve the convergence properties of the Deep Reinforcement Learning algorithm, the parameters of the former layers of the convolutional neural networks are transferred from a convolutional auto-encoder under an unsupervised learning process. The experiment results show that the proposed framework could efficiently extract the fault features from raw time-domain data and have higher accuracy than other deep learning models with balanced samples and better performance with imbalanced samples.
MSWR-LRCN: A new deep learning approach to remaining useful life estimation of bearings
Rolling bearings are important components of industrial rotating machinery and equipment. The prediction of the remaining useful life (RUL) of rolling bearings is of great significance for improving the safety of the machine, reducing the economic and property losses caused by the failure of the bearings. However, for the task of predicting the RUL of rolling bearings, the information of the past time and the future time are as important as the information of the current time. In order to make better use of the extracted features for RUL prediction of rolling bearings, this paper has proposed a novel deep learning framework of multi-scale long-term recurrent convolutional network with wide first layer kernels and residual shrinkage building unit (MSWR-LRCN). The major difference from the previous deep neural network is that our new network organically combines the attention mechanism with multi-scale feature fusion strategy, and improves the anti-noise ability of the entire network. In addition, moving average (MA) method and a polynomial fitting model are also used, which help predict the RUL of rolling bearings effectively. The results show that this method has improved the prediction accuracy compared with the existing methods.
Detecting different fault locations on a bearing
We are going to use the popular Bearing Vibration Data Set from Case Western Reserve University as a benchmark to demonstrate how different bearing conditions and faults can be properly correlated to a different operational mode, and ultimately to the automatic identification of healthy and faulty operational conditions.
MultiViz Vibration’s Mode Identification feature is powered by our Automatic Mode Identification (AMI) unsupervised algorithm for multivariate time series analysis. It performs multidimensional data segmentation and clustering in time series data, such as waveform vibration signals. It detects time periods in which the data exhibits a similar behavior and reports these periods as belonging to the same operational mode.
Operational modes are often correlated with typical conditions of an asset, like on/off, load conditions or fault states. Thus, the identification of different modes when the behavior of the machine has remained the same, can point to the appearance of a fault in the machine.
Predictive Monitoring: Gas Turbines Demo
This Factory Is Using AR To Help With A Hiring Crunch
One of the challenges associated with AR has been in trying to turn a complex physical process, such as wiring a component or working a machine, into code that could run on a headset. Taqtile CEO Dirck Schou said the company’s software makes programming for AR glasses simple, and based on my conversation with Tim Lecrone and Beau Wileman of PBC, the software Taqtile developed is easy to use. Once PBC has created a module for training it pays for itself after 1.44 employees train with it according to Wileman.
The cobots help handle processes that are repetitive and free up people to take on different tasks. Given how tough it is to hire people to work in the factory, using them helps reduce the overall staffing load. But the biggest gains so far have been in training and getting employees quickly up to speed. Now PBC can hire a person and get them working on a machine in a few days as opposed to that taking up to six weeks. It also helps reduce the cost of training a cobot and staff. Wileman told me that an intern, which costs $17 an hour, can train a cobot or map out a process in less than four hours, while it might cost around $30,000 for an outside expert to manually train a cobot.
Efficient federated convolutional neural network with information fusion for rolling bearing fault diagnosis
In the past year, various deep learning-based fault diagnosis methods have been designed to guarantee the stable, safe, and efficient operation of electromechanical systems. To achieve excellent diagnostic performance, the conventional centralized learning (CL) approach is adopted to collect as much data as possible from multiple industrial participants for deep model training. Due to privacy concerns and potential conflicts, industrial participants are unwilling to share their data resources. To solve the issues, this study proposes a fault diagnosis method based on federated learning (FL) and convolutional neural network (CNN), which allows different industrial participants to collaboratively train a global fault diagnosis model without sharing their local data. Model training is locally executed within each industrial participant, and the cloud server updates the global model by aggregating the local models of the participants. Specifically, an adaptive method is designed to adjust the model aggregation interval according to the feedback information of the industrial participants in order to reduce the communication cost while ensuring model accuracy. In addition, momentum gradient descent (MGD) and dropout layer are used to accelerate convergence rate and avoid model overfitting, respectively. The effectiveness of the proposed method is verified on a non-independent and identically distributed (non-iid) rolling bearing fault dataset. The experiment results indicate that the proposed method has higher accuracy than traditional fault diagnosis methods. Moreover, this study provides a promising collaborative training approach to the fault diagnosis field.
Machine Learning Keeps Rolling Bearings on the Move
Rolling bearings are essential components in automated machinery with rotating elements. They come in many shapes and sizes, but are essentially designed to carry a load while minimizing friction. In general, the design consists of two rings separated by rolling elements (balls or rollers). The rings can rotate can rotate relative to each other with very little friction.
The ability to accurately predict the remaining useful life of the bearings under defect progression could reduce unnecessary maintenance procedures and prematurely discarded parts without risking breakdown, reported scientists from the Institute of Scientific and Industrial Research and NTN Next Generation Research Alliance Laboratories at Osaka University.
The scientists have developed a machine learning method that combines convolutional neural networks and Bayesian hierarchical modeling to predict the remaining useful life of rolling bearings. Their approach is based on the measured vibration spectrum.