Smart filter aided domain adversarial neural network: An unsupervised domain adaptation method for fault diagnosis in noisy industrial scenarios

The application of unsupervised domain adaptation (UDA)-based fault diagnosis methods has shown significant efficacy in industrial settings, facilitating the transfer of operational experience and fault signatures between different operating conditions, different units of a fleet or between simulated and real data. However, in real industrial scenarios, unknown levels and types of noise can amplify the difficulty of domain alignment, thus severely affecting the diagnostic performance of deep learning models. To address this issue, we propose an UDA method called Smart Filter-Aided Domain Adversarial Neural Network (SFDANN) for fault diagnosis in noisy industrial scenarios. The proposed methodology comprises two steps. In the first step, we develop a smart filter that dynamically enforces similarity between the source and target domain data in the time-frequency domain. This is achieved by combining a learnable wavelet packet transform network (LWPT) and a traditional wavelet packet transform module. In the second step, we input the data reconstructed by the smart filter into a domain adversarial neural network (DANN). To learn domain-invariant and discriminative features, the learnable modules of SFDANN are trained in a unified manner with three objectives: time-frequency feature proximity, domain alignment, and fault classification. We validate the effectiveness of the proposed SFDANN method based on two fault diagnosis cases: one involving fault diagnosis of bearings in noisy environments and another involving fault diagnosis of slab tracks in a train-track-bridge coupling vibration system, where the transfer task involves transferring from numerical simulations to field measurements. Results show that compared to other representative state of the art UDA methods, SFDANN exhibits superior performance and remarkable stability

Slab Track Condition Monitoring Based on Learned Sparse Features from Acoustic and Acceleration Signals

The implementation of concrete slab track solutions has been recently increasing particularly for high-speed lines. While it is typically associated with low periodic maintenance, there is a significant need to detect the state of slab tracks in an efficient way. Data-driven detection methods are promising. However, collecting large amounts of labeled data is particularly challenging since abnormal states are rare for such safety-critical infrastructure. To imitate different healthy and unhealthy states of slab tracks, this study uses three types of slab track supporting conditions in a railway test line. Acceleration sensors (contact) and acoustic sensors (contactless), are installed next to the three types of slab track to collect the acceleration and acoustic signals as a train passes by with different speeds. We use a deep learning framework based on the recently proposed Denoising Sparse Wavelet Network (DeSpaWN) to automatically learn meaningful and sparse representations of raw high-frequency signals. A comparative study is conducted among the feature learning / extraction methods, and between acceleration signals and acoustic signals, by evaluating the detection effectiveness using a multi-class support vector machine. It is found that the classification accuracy using acceleration signals can reach almost 100%, irrespective which feature learning / extraction method is adopted. Due to the more severe noise interference in acoustic signals, the performance of using acoustic signals is worse than of using acceleration signals. However, it can be significantly improved by leaning meaningful features with DeSpaWN

Comparison of vibration and noise characteristics of urban rail transit bridges with box-girder and U-shaped sections

Rolling noise and structure-borne noise from rail transit viaducts often lead to complaints from nearby residents. Concrete box section bridges are commonly adopted for the viaducts but those with U-shaped sections have become popular recently due to their more attractive form and reduced visual impact. One question that often arises in relation to the choice of the section types is their relative noise performance. This study aims to compare the vibration and noise characteristics of concrete bridges with different sections in a systematic way by using the same noise prediction method. A coupled track-bridge model is introduced to obtain the rail vibration and the power input to the bridge through the rail fasteners. A three-dimensional vibro-acoustic finite element method is applied to obtain the noise radiated from the bridge and the rail subjected to sets of multiple forces acting on them. This is determined in terms of the radiation efficiency and sound pressure transfer functions for arbitrary forcing. The averaged mean squared vibration velocity of the coupled wheel-track-bridge model subjected to roughness excitation is used to scale the noise from the acoustic model. The method is validated by comparison with field measurements of noise from a U-shaped bridge in Shanghai. Comparative investigations are then conducted of the U-shaped bridge, a box girder bridge with single cell and a twin-box girder bridge with two cells; each is fitted with equivalent noise barriers. It is found in each case that the noise from the rail is about 10 dB(A) larger than the bridge noise at positions to the side of the bridge. The U-shaped girder generally leads to slightly lower total noise levels than the box girders, with differences of less than 1 dB(A) when they have noise barriers of the same height. In terms of the bridge noise, however, the single-box and twin-box girders produce an average of 8.6 and 11.7 dB(A) less noise than the U-shaped girder.<br/

Improved indirect measurement of the dynamic stiffness of a rail fastener and its dependence on load and frequency

The dynamic stiffness of rail fasteners has a significant effect on the noise radiated by the rails as well as the vibration transmitted to the underlying structures, such as bridges, tunnels or track at grade. This study investigates the load- and frequency-dependence of a WJ-2A fastener which is commonly used in urban rail transit systems in China. This is a two-stage fastener with a rail pad and a baseplate pad separated by a steel plate. Results are obtained using the indirect measurement method in the frequency range 30 to 1000 Hz. The dynamic stiffness of the individual components is investigated as well as that of the whole fastener system and the combined stiffness of the components is verified by comparison with the whole fastener system. A numerical model of the test rig is used to provide understanding of various artefacts that are observed in the measurements and corrections are proposed to minimise their effects on the measured results. These allow for the differences between the response at the measurement positions and at the ideal positions at the interfaces between the fastener system and the rig. The stiffness magnitude of the rail pad, baseplate pad, and whole fastener system increases strongly with increasing static preload and increases weakly with increasing frequency; it is important to take these effects into account in prediction models for noise and vibration. The damping loss factors are not strongly dependent on preload or frequency. To describe the frequency-dependence, a fractional derivative Kelvin-Voigt (FDKV) model is introduced and is fitted to the dynamic stiffness with the help of a genetic algorithm method.The dynamic stiffness of the whole fastener system is influenced by both the rail pad and the lower baseplate pad. It is important to take account of both of them, as well as the internal resonance of the baseplate assembly which appears at around 1 kHz

Engineering a HemoMap Nanovaccine for Inducing Immune Responses against Melanoma

Neoantigen vaccines have opened a new paradigm for cancer immunotherapy. Here, we constructed a neoantigen nanovaccine-HemoMap, with the ability to target lymph nodes and activate immune cells. We propose a HemoMap nanovaccine consisting of the mouse melanoma highly expressed antigenic peptide Tyrp1 and a magnesium nanoadjuvant-HemoM. By immunofluorescence labeling of the nanovaccine, the lymph node targeting of the vaccine was observed and verified by a mouse near-infrared imaging system. About two-fold higher effective retention of HemoMap induces the internalization of Tyrp1 in DCs than that of free Tyrp1 in draining lymph nodes (DLNs) for 48 h. A mouse melanoma subcutaneous model was established to evaluate neoantigen-specific antitumor immune responses. In comparison to the control group, the tumor growth rate was dramatically slowed down by HemoMap treatment, and the median survival time was extended by 7 days. We discovered that effective co-delivery of Tyrp1 antigen and magnesium (Mg2+) to lymph nodes (LNs) boosted cellular internalization and activated immune cells, such as CD11c+ DCs and CD8+ T lymphocytes. Spleen lymphocytes from the HemoMap group displayed much more antitumor activity than those from the other groups. Our findings highlight that HemoMap is promising to trigger T cell responses and to provide novel nanoadjuvants strategies for cancer immunotherapy

Lack of association of single nucleotide polymorphism in LRCH1 with knee osteoarthritis susceptibility

A genetic association of knee osteoarthritis (OA) and a C/T transition single nucleotide polymorphism (SNP) (rs912428) located in intron 1 of the LRCH1 gene has recently been reported in European Caucasians; however, the results are inconsistent. Our objective was to evaluate the association in different knee OA populations. Three case-control association studies were conducted in Han Chinese, Japanese, and Greek Caucasian populations. The LRCH1 SNP was genotyped in patients who had primary symptomatic knee OA with radiographic confirmation and in matched controls, and the association was examined. We performed a meta-analysis for the studies together with results of two previous papers using the DerSimonian-Laird procedure and calculated the power of the pooled studies by the software R. A total of 1,145 OA patients and 1,266 controls were genotyped. No significant difference was detected in genotype or allele frequencies between knee OA and control groups in the three populations (all P > 0.05). Association was not observed even after stratification by gender and Kellgren/Lawrence (K/L) scores. Meta-analysis also supported the lack of association between LRCH1 and knee OA. The strong heterogeneity between original and replication studies was detected in Caucasian populations. However, a tendency for the increase of TT genotype was observed in the European populations (OR = 1.46, P = 0.06). The powers for European and Asian replication studies were less than 0.8. Our results suggest that there is no association between LRCH1 and knee OA. However, lack of association should be concluded by further replication studies