Real-time Fault Detection Research Articles

A New Approach to Detect Power Quality Disturbances in Smart Cities Using Scaling-Based Chirplet Transform with Strategically Placed Smart Meters

The growth of Internet of Things (IoT)-enabled devices has increased the amount of data created by the distribution network’s periphery nodes, requiring more data transfer capacity. Recent applications’ real-time requirements have strained standard computing paradigms, and data processing has struggled to keep up. Edge computing is employed in this research to detect distribution network faults, allowing for instant sensing and real-time reaction to the control room for faster investigation of distribution problems and power outages, making the system more reliable. Moreover, to overcome the challenges of fault detection, advanced signal processing methods need to be integrated with the Adaboost classifier. An Adaboost-based edge device, suitable for installation on top of a power pole, is proposed in this research as a means of real-time fault detection. To increase throughput, decrease latency and offload network traffic, data collecting, feature extraction and Adaboost-based problem identification are all performed in an integrated edge node. Enhanced detection accuracy (98.67%) and decreased latency (115.2 ms) verify the effectiveness of the suggested approach. In this research, we enhance the classical chirplets transform to create the scaling-basis chirplet transform (SBCT) for time–frequency (TF) analysis. This approach modulates the TF basis around the relevant time function to modify the chirp rate with frequency and time. By carefully selecting the sampling frequency, it is possible to discriminate between short circuit fault and high-impedance fault (HIF) by calculating spectral entropy. The TF representation obtained with the SBCT provides considerably higher energy concentrations, even for signals with numerous components, closely spaced frequencies and heavy background noise.

Real-Time DC Series Arc Fault Detection Based on Noise Pattern Analysis in Photovoltaic System

This paper presents a method for detecting series arc faults based on noise pattern analysis in photovoltaic systems. The arc detection circuit is required to detect the series arc quickly and not falsely detect the system noise as arc noise. This paper proposes noise pattern analysis to distinguish between system noise and arc noise and prevent false detection. First, periodic feature analysis prevents false detection by periodic noise, such as inverter noise, using the difference in periodic characteristics. Second, zero-range density analysis detects series arc noise using the difference in fluctuation characteristics. An integrated arc fault detection algorithm comprising discrete wavelet transform decomposition, periodic feature analysis, zero-range density, and an iterative loop algorithm is developed. The proposed methods are verified using noise distinction experiments, wherein the injected signal and arc noise are distinguished with high classification precision using the proposed noise pattern analysis method. The developed arc detection algorithm is applied to a real-time series arc detection board based on the TMS320F28335 DSP, and the performance of the series arc detection and false detection prevention is verified using the UL1699B arc detection test circuit and a real PV system.

Integration of Artificial Intelligence for Real-Time Fault Detection in Semiconductor Packaging

Semiconductor manufacturing involves a complex sequence of unit processes, where even a minor error can disrupt the entire production chain. Present-day manufacturing setups rely on continuous data monitoring of equipment health, wafer measurements, and inspections to identify any abnormalities that could impact the quality and performance of the final chip. The primary aim is fault detection and classification (FDC), for which a range of techniques including statistical analysis and machine learning algorithms are commonly employed. In this study, we introduce an innovative approach utilizing an artificial immune system (AIS), inspired by biological mechanisms, for FDC in semiconductor equipment. The main culprits behind process failures are shifts caused by the aging of parts and modules over time. Our methodology integrates state variable identification (SVID) data, reflecting current equipment conditions, and optical emission spectroscopy (OES) data, capturing plasma process information under faulty scenarios induced by deliberate gas flow rate adjustments in semiconductor fabrication. Our results demonstrate a modeling prediction accuracy of 94.69% when incorporating selected SVID and OES data, and 93.68% accuracy using OES data alone. In conclusion, we suggest the potential application of AIS in semiconductor process decision-making, offering promising avenues for enhancing fault detection in semiconductor equipment.

Inline Bondwave Monitoring for Direct Bonding, Process Optimization and Impact on Post-Bond Distortion

Direct wafer bonding is essential in semiconductor manufacturing, with bondwave propagation dynamics influencing the overall bond quality. The main factor impacting bondwave velocity dynamics are substrate rigidity, fluid viscosity and adhesion energy between the two surfaces. The latter, which depends on substrates properties and process parameters, is key and should be controlled and monitored for fusion bonding applications. The use of an inline infrared (IR) monitoring within an automated fusion bonding equipment, EVG®850LT, facilitates real-time defect detection, root cause analysis, and bond re-workability. The equipment, which features a sealed bonding chamber equipped with an IR camera and a reflective chuck, together with a plasma chamber for pre-processing enables the assessment of various process conditions. Post-bond distortions are then evaluated using high-resolution metrology. Bondwave monitoring use-cases, a detailed analysis of bondwave speed impact on bonded stack distortion and the influence of diverse process parameters on bondwave propagation are presented or discussed in the current paper.

Explainable deep neural network for in-plain defect detection during additive manufacturing

PurposeThe purpose of this study is to develop a deep learning framework for additive manufacturing (AM), that can detect different defect types without being trained on specific defect data sets and can be applied for real-time process control.Design/methodology/approachThis study develops an explainable artificial intelligence (AI) framework, a zero-bias deep neural network (DNN) model for real-time defect detection during the AM process. In this method, the last dense layer of the DNN is replaced by two consecutive parts, a regular dense layer denoted (L1) for dimensional reduction, and a similarity matching layer (L2) for equal weight and non-biased cosine similarity matching. Grayscale images of 3D printed samples acquired during printing were used as the input to the zero-bias DNN.FindingsThis study demonstrates that the approach is capable of successfully detecting multiple types of defects such as cracks, stringing and warping with high accuracy without any prior training on defective data sets, with an accuracy of 99.5%.Practical implicationsOnce the model is set up, the computational time for anomaly detection is lower than the speed of image acquisition indicating the potential for real-time process control. It can also be used to minimize manual processing in AI-enabled AM.Originality/valueTo the best of the authors’ knowledge, this is the first study to use zero-bias DNN, an explainable AI approach for defect detection in AM.

Weight-guided feature fusion and non-local balance model for aluminum surface defect detection

Aluminum surface defect detection plays a crucial role in the manufacturing industry. Due to the complexity of aluminum surface defects, the existing defect detection methods have false and missed detection problems. To address the characteristics of aluminum surface defects and the problems of existing methods, we propose a weight-guided feature fusion and non-local balance model to improve the detection effect. Firstly, we design the feature extraction network cross-stage partial ConvNeXt, which achieves adequate feature extraction while reducing the model’s size. In addition, we propose a weight-guided feature fusion and non-local balanced feature pyramid (WBFPN). Specifically, we design a weight-guided feature fusion module to replace the simple feature fusion method so that the WBFPN can suppress interference information when fusing feature maps at different scales. The non-local balancing module captures the long-range dependencies of image features and effectively balances small target defects’ detail and semantic information. Finally, the confidence loss was redefined to effectively solve the problem of poor detection effect caused by the imbalance of positive and negative samples. Experimental results show that the average accuracy of the proposed model reaches 91.9%, and the detection speed is high, which meets the requirement of real-time defect detection.

Aquaculture defects recognition via multi-scale semantic segmentation

Aquaculture net pen defects such as biofouling, vegetation, and holes are key challenges to efficient and sustainable fish production in aquaculture. These defects must be monitored to avoid problems with aquaculture net safety as well as fish growth. Recently, deep learning methods have been adopted to solve detection and classification problems in different applications. However, the conventional methods are challenging to meet the demands of high precision and real-time detection of aquaculture net defects in a complex marine environment. Towards this end, this paper proposes an autonomous net pen defect detection system that contains a novel multi-scale semantic segmentation topology for detecting the biofouling, vegetation, and hole problems in the aquaculture environment. In particular, we emphasize fusing the attention maps obtained across different decomposition levels of the network to generate rich feature distributions that enable the accurate extraction of biofouling, vegetation, and holes within aquaculture nets. Moreover, the proposed model is thoroughly tested on two private datasets and two public datasets which were acquired from a real-field fish farms. Across all four datasets, the proposed framework showed remarkable biofouling, vegetation, and hole detection performance, where it outperformed state-of-the-art methods by 6.58%, 3.69%, 6.44%, and 4.78% in terms of mean average precision across LABUST, KU, NDv1, and NDv2 datasets, respectively.

EFFNet: Element-wise feature fusion network for defect detection of display panels

Online or real-time defect detection of display panels after array process is of paramount importance for quality control and yield rate improvement of products in display industry. However, owing to the limitation in feature representation, the performances of traditional defect detection methods are not satisfactory. This paper develops a novel element-wise feature fusion network (EFFNet) to solve the issue and achieve high-accuracy real-time defect detection of display panels. The method adopts a transfer learning and fine-tuning strategy for feature extraction layers and a decoder with relatively less computational complexity. Particularly, a feature fusion module based on element-wise addition of pyramid features is proposed in skip connection to improve detection efficiency and accuracy. Our method is compared with many state-of-the-art CNN-based models. Additionally, the effects of training dataset size, motion blur, and different backgrounds on the performance of the proposed method are investigated. Extensive experiments, including the ablation study, demonstrate that the developed network can accurately detect defects with complex textures, ambiguous boundaries and low contrast. It also has good robustness against motion blur. It outperforms state-of-the-art methods in terms of mIoU, mPA, and F1-Measure. Moreover, it is able to detect defects at speeds of up to 159 fps with input images of size 256 × 256 pixels.

A New Deep Learning Framework for Imbalance Detection of a Rotating Shaft.

Rotor unbalance is the most common cause of vibration in industrial machines. The unbalance can result in efficiency losses and decreased lifetime of bearings and other components, leading to system failure and significant safety risk. Many complex analytical techniques and specific classifiers algorithms have been developed to study rotor imbalance. The classifier algorithms, though simple to use, lack the flexibility to be used efficiently for both low and high numbers of classes. Therefore, a robust multiclass prediction algorithm is needed to efficiently classify the rotor imbalance problem during runtime and avoid the problem's escalation to failure. In this work, a new deep learning (DL) algorithm was developed for detecting the unbalance of a rotating shaft for both binary and multiclass identification. The model was developed by utilizing the depth and efficacy of ResNet and the feature extraction property of Convolutional Neural Network (CNN). The new algorithm outperforms both ResNet and CNN. Accelerometer data collected by a vibration sensor were used to train the algorithm. This time series data were preprocessed to extract important vibration signatures such as Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT). STFT, being a feature-rich characteristic, performs better on our model. Two types of analyses were carried out: (i) balanced vs. unbalanced case detection (two output classes) and (ii) the level of unbalance detection (five output classes). The developed model gave a testing accuracy of 99.23% for the two-class classification and 95.15% for the multilevel unbalance classification. The results suggest that the proposed deep learning framework is robust for both binary and multiclass classification problems. This study provides a robust framework for detecting shaft unbalance of rotating machinery and can serve as a real-time fault detection mechanism in industrial applications.

Infrared in-line monitoring of flaws in steel welded joints: a preliminary approach with SMAW and GMAW processes

The non-destructive full-field non-contact thermographic technique is applied for non-destructive flaw detection of the welded joints, in real-time and offline configuration. In this paper, a thermographic procedure for real-time flaw detection in manual arc welding process is presented. Surface temperature acquisitions by means of an IR camera were performed during arc welding process of 8 specimen both for calibration and validation of the numerical model. The investigated variables are the technique (manual stick arc (SMAW) and gas arc (GMAW) welding) and the joint shape (butt and T joint) for steel joints, in sound conditions and with artificial flaws. Numerical simulation of welding thermal transients was run to obtain the expected surface temperature fields and thermal behavior for different welding parameter configurations. Hardness measurement and micro-graphic analysis were performed to validate numerical simulation results. The real-time thermographic study of the weld pool gives direct indications of anomalies; local studies of the thermal transient and thermal profiles can detect some kind of flaws; microstructural analysis of Heat-Affected Zone (HAZ) and surrounding areas higlights the presence of austenite and martensite distribution which justifies the thermal transients and thermal profiles for different welding configurations. Comparing real-time IR acquisition of the welding process with simulated thermal contours of sound processes provides information of presence of some kind of flaws. Since most of the flaws are generated in the weld pool, it is possible to recognize anomalies directly from the thermal acquisitions or with post-processing the acquired data.

Online detection of interturn short-circuit fault in induction motor based on 5th harmonic current tracking using Vold-Kalman filter

<span lang="EN-US">In this paper we propose a strategy for real-time detection of interturn short-circuit faults (ISCF) on three-phase induction motor (IM) by using a Vold-Kalman filter (VKF) algorithm. ISCF produce a thermal stress into the stator winding due to large current that flows through the short-circuited turns. Therefore, incipient fault detection is required in order to avoid catastrophic failures such as phase to phase, or phase to ground faults. The strategy is based on an analytical IM model that includes a ISCF fault in any of the phase windings and considering the </span><em><span lang="EN-US">h<sup>-th</sup></span></em><span lang="EN-US"> harmonic in the voltage supply. Based on equivalent electrical circuits with harmonics in sequence components, we propose a strategy for detection of an ISCF on IM by tracking the 5<sup>th</sup> harmonic current component using a VKF algorithm. The proposed model is experimentally validated using a three-phase IM with modified stator windings to generate ISCF. Also, the IM is feeded by a programmable voltage source to synthesize distorted voltage supply with the 5<sup>th</sup> harmonic. The results demonstrated that the positive-sequence magnitude for the 5<sup>th</sup> harmonic current component is a good indicator of the fault severity once it exceeds a threshold limit value, even under load variations and unbalanced voltages.</span>

Development of image-based fast defect recognition and localization network (FDRLNet) for steel surfaces

The utilization of vision-based systems for automated inspection and quality control tasks increased in recent years due to the adaptation of Industry 4.0 initiatives in manufacturing. Continuous steel production units extensively employ vision-based solutions to classify and localize surface defects. The inspection system must provide quick and reliable feedback about surface defect type (classification) and location (localization) utilizing images acquired from the camera. This paper presents a novel surface defect detector, Fast Defect Recognition and Localization Network (FDRLNet), for achieving accurate prediction abilities at higher inference speeds. The robustness of the proposed approach is validated by utilizing publicly available Northeastern University (NEU-DET) surface defect dataset. The prediction abilities are corroborated by comparing the mean Average Precision (mAP) and inference speed with other competitive approaches presented in the literature. It has been shown that the proposed approach has robust prediction abilities at higher inference speeds and can be implemented in real-time defect detection tasks.

Real-Time Defect Detection Scheme Based on Deep Learning for Laser Welding System

Laser welding, as an important material processing technology, has been widely used in various fields of industry. In most industrial welding production and processing, high precision is required for welding parameters and fixed work pieces. However, in the process of laser welding, serious heat transfer effect will bring unpredictable welding deviations, and even a small deviation will lead to serious welding defects, which will affect the quality of the welded products. Traditional non-destructive testing methods have been widely used, but they have been proved to have some limitations. Existing laser welding defect detection schemes are mainly focused on the detection of post-weld defects, which requires a large amount of data, and the real-time detection cannot be guaranteed. In this paper, we propose a data acquisition system for collecting changes in physical characteristics during laser welding with the aids of multiple sensors. Based on the data originating from sensors’ system, an efficient laser welding defect detection model has been designed and investigated based on the MSCNN (multi-scale convolutional neural network), BiLSTM (bidirectional long short-term memory), and AM (attention mechanism). The final proposed MSCNN-BiLSTM-AM fusion detection model can achieve 99.38% detection accuracy, which make the laser welding system more efficient and more suitable.

MSDD-YOLOX: An enhanced YOLOX for real-time surface defect detection of oranges by type

Using an online high-throughput detection system for sorting oranges during the post-harvest process helped improve the commercialization level of the oranges industry. Surface defects on oranges created a poor first impression for consumers, making the rapid detection of oranges surface defects a primary concern for online sorting systems. However, due to variations in defect size and the visual similarity of different defects, there were still some challenges in detecting and identifying various surface defects on orange fruits based on their types. To address these challenges, this study first categorized surface defects on oranges into three major categories: deformity, scarring, and disease spot, based on their causes and potential post-harvest losses. Subsequently, to achieve real-time detection of orange surface defects on the orange sorting machine, a YOLOX-based real-time multi-type surface defect detection algorithm (MSDD-YOLOX) for oranges was proposed. This algorithm significantly improved the detection effectiveness of scarring at different scales by introducing neck network residual connections and cascading of the neck network. To address the issue of missed detections in texture-based defects and improve the regression of predicted bounding boxes, focal loss and Complete-IoU (CIoU) were employed in the algorithm. The results showed that MSDD-YOLOX achieved F1 values of 88.3 %, 80.4 %, and 92.7 % for the detection of deformity, scarring, and disease spot, respectively, with an overall detection F1 value of 90.8 %. These values represented improvements of 13.1 %, 10.2 %, 4.5 %, and 6.4 %, respectively, compared to the baseline model. Furthermore, compared to other deep learning object detectors, namely Faster RCNN, RetinaNet, FCOS, and Swin-Transformer, the proposed algorithm achieved optimal detection accuracy. Additionally, the MSDD-YOLOX model had a compact size of only 8.98 M, enabling real-time detection on the fruit grading line with an inference speed of up to 64.2FPS. Another innovation of this research was the external validation conducted on green oranges from Hainan and mandarins from Zhejiang. The results of external testing demonstrated that MSDD-YOLOX achieved overall F1 values of 90.6 % and 81.1 % for citrus fruits in these two regions, effectively proving the online deployment capability of MSDD-YOLOX and providing a robust solution for external defect detection in citrus fruits.

Research on Wind Turbine Fault Detection Based on the Fusion of ASL-CatBoost and TtRSA.

The internal structure of wind turbines is intricate and precise, although the challenging working conditions often give rise to various operational faults. This study aims to address the limitations of traditional machine learning algorithms in wind turbine fault detection and the imbalance of positive and negative samples in the fault detection dataset. To achieve the real-time detection of wind turbine group faults and to capture wind turbine fault state information, an enhanced ASL-CatBoost algorithm is proposed. Additionally, a crawling animal search algorithm that incorporates the Tent chaotic mapping and t-distribution mutation strategy is introduced to assess the sensitivity of the ASL-CatBoost algorithm toward hyperparameters and the difficulty of manual hyperparameter setting. The effectiveness of the proposed hyperparameter optimization strategy, termed the TtRSA algorithm, is demonstrated through a comparison of traditional intelligent optimization algorithms using 11 benchmark test functions. When applied to the hyperparameter optimization of the ASL-CatBoost algorithm, the TtRSA-ASL-CatBoost algorithm exhibits notable enhancements in accuracy, recall, and other performance measures compared with the ASL-CatBoost algorithm and other ensemble learning algorithms. The experimental results affirm that the proposed algorithm model improvement strategy effectively enhances the wind turbine fault detection classification recognition rate.

Surface Defect Detection for Automated Tape Laying and Winding Based on Improved YOLOv5.

To address the issues of low detection accuracy, slow detection speed, high missed detection rate, and high false detection rate in the detection of surface defects on pre-impregnated composite materials during the automated tape laying and winding process, an improved YOLOv5 (You Only Look Once version 5) algorithm model was proposed to achieve the high-precision, real-time detection of surface defects. By leveraging this improvement, the necessity for frequent manual interventions, inspection interventions, and subsequent rework during the automated lay-up process of composite materials can be significantly reduced. Firstly, to improve the detection accuracy, an attention mechanism called "CA (coordinate attention)" was introduced to enhance the feature extraction ability, and a Separate CA structure was used to improve the detection speed. Secondly, we used an improved loss function "SIoU (SCYLLA-Intersection over Union) loss" to replace the original "CIoU (Complete-Intersection over Union) loss", which introduced an angle loss as a penalty term to consider the directional factor and improve the stability of the target box regression. Finally, Soft-SIoU-NMS was used to replace the original NMS (non-maximum suppression) of YOLOv5 to improve the detection of overlapping defects. The results showed that the improved model had a good detection performance for surface defects on pre-impregnated composite materials during the automated tape laying and winding process. The FPS (frames per second) increased from 66.7 to 72.1, and the mAP (mean average precision) of the test set increased from 92.6% to 97.2%. These improvements ensured that the detection accuracy, as measured by the mAP, surpassed 95%, while maintaining a detection speed of over 70 FPS, thereby meeting the requirements for real-time online detection.

An adaptive law based online system identification of cropped delta unmanned aerial vehicles from flight tests

Online system identification of an aircraft has multifaceted advantages, such as real-time health monitoring, fault detection and reconfiguration of control, to name a few. Researchers have utilized variants of the equation error method, which involves non-gradient-based computation to carry out online system identification, due to its feasibility for real-time implementation. However, it was found to be underperforming while handling high angles of attack as well as time-varying bias parameters. In the current manuscript, an attempt has been made to alleviate the aforementioned limitation by proposing a novel online system identification technique based on adaptive law and its applicability is demonstrated by using compatible flight data sets, acquired from two cropped delta wing unmanned aerial vehicles, pertaining to linear, moderately nonlinear, and near stall flight regimes. It is observed that the proposed method is able to predict unknown aerodynamic derivatives, irrespective of flight envelopes, if dynamic modes are adequately excited. Confidence in the estimated aerodynamic parameters is assured with the two-sigma error bound. It is noticed that the maximum relative standard deviation in the estimates obtained with the proposed method is less than the recursive least squares method and higher than the sequential least squares-recursive Fourier transform method in the linear flight regime. Simulated responses using the estimates obtained from aforementioned methods are compared with measured flight data, and it is witnessed that the simulated nondimensional lift coefficient with the proposed method has a relative offset of less than 34.2% w.r.t measured value in near-stall flight regimes, which is least among all three methods.

Fault Detection in Power Distribution Networks Based on Comprehensive-YOLOv5.

Real-time fault detection in power distribution networks has become a popular issue in current power systems. However, the low power and computational capabilities of edge devices often fail to meet the requirements of real-time detection. To overcome these challenges, this paper proposes a lightweight algorithm, named Comprehensive-YOLOv5, for identifying defects in distribution networks. The proposed method focuses on achieving rapid localization and accurate identification of three common defects: insulator without loop, cable detachment from the insulator, and cable detachment from the spacer. Based on the You Only Look Once version 5 (YOLOv5) algorithm, this paper adopts GhostNet to reconstruct the original backbone of YOLOv5; introduces Bidirectional Feature Pyramid Network (BiFPN) structure to replace Path Aggregation Network (PANet) for feature fusion, which enhances the feature fusion ability; and replaces Generalized Intersection over Union GIOU with Focal Extended Intersection over Union (Focal-EIOU) to optimize the loss function, which improves the mean average precision and speed of the algorithm. The effectiveness of the improved Comprehensive-YOLOv5 algorithm is verified through a "morphological experiment", while an "algorithm comparison experiment" confirms its superiority over other algorithms. Compared with the original YOLOv5, the Comprehensive-YOLOv5 algorithm improves mean average precision (mAP) from 88.3% to 90.1% and increases Frames per second (FPS) from 20 to 52 frames. This improvement significantly reduces false positives and false negatives in defect detection. Consequently, the proposed algorithm enhances detection speed and improves inspection efficiency, providing a viable solution for real-time detection and deployment at the edge of power distribution networks.

An automated optical inspection (AOI) platform for three-dimensional (3D) defects detection on glass micro-optical components (GMOC)

With the widespread deployment of wavelength division multiplexing (WDM), optical transceivers increasingly use many glass micro-optical components (GMOC). Visual inspection of these GMOCs is a critical manufacturing step to ensure quality and reliability. However, manual inspection is often labor-intensive and time-consuming due to the transparent nature of glass components and the small, randomly located defects in three dimensions. Although automated optical inspection (AOI) exists, it has not yet been able to provide the desired level of accuracy and efficiency.This paper reports the development of an AOI platform for 3D defect detection on GMOCs. The platform incorporates 3D video acquisition and a novel two-stage neural network machine-learning algorithm. It includes a robotic arm for moving parts in 3D, a camera with an illumination module for video acquisition, and a video streaming processing unit with a machine vision algorithm for real-time defect detection on a production line. The robotic arm enables multi-perspective video capture of a test sample without refocusing. The two-stage machine learning network uses a modified YOLOv4 architecture with color channel separation (CCS) convolution, an image quality evaluation (IQE) module, and a frame fusion module to integrate the single frame detection results. This network can process multi-perspective video streams in real-time for defects detection in a coarse-to-fine manner. The AOI platform was trained with only 30 samples and achieved promising performances with a recall rate of 1, a detection accuracy of 97%, and an inspection time of 48 s per part.

Prediction of Bearing Vibration Fault State based on Fused Bi-LSTM and SVM

Machinery failure prediction and equipment health management is the key to reduce even prevent the occurrence of significant failures. In recent years, deep learning based intelligent fault diagnosis methods have become a hot topic of research in the field of machinery and equipment health management, especially the evaluation of the equipment health indicators and failure mode identification issues in complex environments. In this paper, a fused bidirectional long and short term memory (BiLSTM) and support vector machine (SVM) recurrent neural network deep learning algorithm is proposed to solve the problem of fast fault diagnosis of mechanical bearing vibration in complex environments. The algorithm uses a BiLSTM memory network as a feature extractor to capture fault features in the raw one dimensional (1-D) time series data, and then a SVM is used at the end of the network instead of the traditional softmax function as a classifier discriminator to further improve the accuracy of diagnosis. The experimental results indicate that the proposed algorithm model has higher diagnostic accuracy and faster diagnosis speed, and more suitable for fast diagnosis and real time detection of faults in complex environments.