Improved bacterial foraging optimization for structural damage identification of bridge erecting machine

Aiming at structure damage characteristics of bridge erecting machine, a vibration-based identification model of structural damage acted as a constrained problem is established. In view of the crack damage, the natural frequency of vibration signal and modal assurance criterion is as the index of damage detection, and then an improved bacterial foraging optimization (NBFO) based on the chemotaxis strategy with normal distribution is proposed, meanwhile it is applied to optimizing the identification model of structural damage. Finally using the girder of TLJ900 bridge erecting machine as an example, the simulation results show that the proposed method can more accurately judge the damage position and degree of structure than its counterparts

RVM-based adaboost scheme for stator interturn faults of the induction motor

This paper presents an AdaBoost method based on RVM (Relevance Vector Machine) to detect and locate an interturn short circuit fault in the stator windings of IM (Induction Machine). This method is achieved through constructing an Adaboost combined with a weak RVM multiclassifier based on a binary tree, and the fault features are extracted from the three phase shifts between the line current and the phase voltage of IM by establishing a global stator faulty model. The simulation results show that, compared with other competitors, the proposed method has a higher precision and a stronger generalization capability, and it can accurately detect and locate an interturn short circuit fault, thus demonstrating the effectiveness of the proposed method

Parameters Optimization for a Kind of Dynamic Vibration Absorber with Negative Stiffness

A new type of dynamic vibration absorber (DVA) with negative stiffness is studied in detail. At first, the analytical solution of the system is obtained based on the established differential motion equation. Three fixed points are found in the amplitude-frequency curves of the primary system. The design formulae for the optimum tuning ratio and optimum stiffness ratio of DVA are obtained by adjusting the three fixed points to the same height according to the fixed-point theory. Then, the optimum damping ratio is formulated by minimizing the maximum value of the amplitude-frequency curves according to H∞ optimization principle. According to the characteristics of negative stiffness element, the optimum negative stiffness ratio is also established and it could still keep the system stable. In the end, the comparison between the analytical and the numerical solutions verifies the correctness of the analytical solution. The comparisons with three other traditional DVAs under the harmonic and random excitations show that the presented DVA performs better in vibration absorption. This result could provide theoretical basis for optimum parameters design of similar DVAs

Improved method for detecting weak abrupt information based on permutation entropy

As a dynamic detecting method for abrupt information, permutation entropy could effectively reflect the subtle change in time series data, which is also simple and can be computed conveniently. Based on the permutation entropy, some improved methods for detecting weak abrupt information hidden in time series data are presented, such as permutation entropy spectrum, second permutation entropy, and second permutation entropy spectrum. Through some simulation examples, these new methods are compared with the existing single permutation entropy and approximate entropy, and the results show that these methods can more effectively detect the much weaker abrupt information. Especially, the second permutation entropy spectrum is very robust even if the periodic abrupt information is very weak

Effect of unconventional oilseeds (safflower, poppy, hemp, camelina) on in vitro ruminal methane production and fermentation

BACKGROUND: Dietary supplementation with oilseeds can reduce methane emission in ruminants, but only a few common seeds have been tested so far. This study tested safflower (Carthamus tinctorius), poppy (Papaver somniferum), hemp (Cannabis sativa), and camelina (Camelina sativa) seeds in vitro using coconut (Cocos nucifera) oil and linseed (Linum usitatissimum) as positive controls. RESULTS: All the tested oilseeds suppressed methane yield (mL g-1 dry matter, up to 21%) compared to the non-supplemented control when provided at 70 g oil kg-1 dry matter, and they were as effective as coconut oil. Safflower and hemp were more effective than linseed (21% and 18% vs. 10%), whereas the effects of poppy and camelina were similar to linseed. When methane was related to digestible organic matter, only hemp and safflower seeds and coconut oil were effective compared to the non-supplemented control (up to 11%). The level of methanogenesis and the ratios of either the n-6:n-3 fatty acids or C18:2 :C18:3 in the seed lipids were not related. CONCLUSION: Unconventional oilseeds widen the spectrum of oilseeds that can be used in dietary methane mitigation. In vivo confirmation of their methane mitigating effect is still needed, and their effects on animal performance still must be determined. © 2017 Society of Chemical Industry

Contribution of ruminal fungi, archaea, protozoa, and bacteria to the methane suppression caused by oilseed supplemented diets

Dietary lipids can suppress methane emission from ruminants, but effects are variable. Especially the role of bacteria, archaea, fungi and protozoa in mediating the lipid effects is unclear. In the present in vitro study, archaea, fungi and protozoa were selectively inhibited by specific agents. This was fully or almost fully successful for fungi and protozoa as well as archaeal activity as determined by the methyl-coenzyme M reductase alpha subunit gene. Five different microbial treatments were generated: rumen fluid being intact (I), without archaea (-A), without fungi (-F), without protozoa (-P) and with bacteria only (-AFP). A forage-concentrate diet given alone or supplemented with crushed full-fat oilseeds of either safflower (Carthamus tinctorius) or poppy (Papaver somniferum) or camelina (Camelina sativa) at 70 g oil kg-1 diet dry matter was incubated. This added up to 20 treatments with six incubation runs per treatment. All oilseeds suppressed methane emission compared to the non-supplemented control. Compared to the non-supplemented control, -F decreased organic matter (OM) degradation, and short-chain fatty acid concentration was greater with camelina and safflower seeds. Methane suppression per OM digested in -F was greater with camelina seeds (-12 vs.-7% with I, P = 0.06), but smaller with poppy seeds (-4 vs. -8% with I, P = 0.03), and not affected with safflower seeds. With -P, camelina seeds decreased the acetate-to-propionate ratio and enhanced the methane suppression per gram dry matter (18 vs. 10% with I, P = 0.08). Hydrogen recovery was improved with -P in any oilseeds compared to non-supplemented control. No methane emission was detected with the -A and -AFP treatments. In conclusion, concerning methanogenesis, camelina seeds seem to exert effects only on archaea and bacteria. By contrast, with safflower and poppy seeds methane was obviously reduced mainly through the interaction with protozoa or archaea associated with protozoa. This demonstrated that the microbial groups differ in their contribution to the methane suppressing effect dependent on the source of lipid. These findings help to understand how lipid supplementation and microbial groups interact, and thus may assist in making this methane mitigation tool more efficient, but await confirmation in vivo

Deep Subdomain Transfer Learning with Spatial Attention ConvLSTM Network for Fault Diagnosis of Wheelset Bearing in High-Speed Trains

High-speed trains operate under varying conditions, leading to different distributions of vibration data collected from the wheel bearings. To detect bearing faults in situations where the source and target domains exhibit differing data distributions, the technique of transfer learning can be applied to move the distribution of features gleaned from unlabeled data in the source domain. However, traditional deep transfer learning techniques do not take into account the relationships between subdomains within the same class of different domains, resulting in suboptimal transfer learning performance and limiting the use of intelligent fault diagnosis for wheel bearings under various conditions. In order to tackle this problem, we have developed the Deep Subdomain Transfer Learning Network (DSTLN). This innovative approach transfers the distribution of features by harmonizing the subdomain distributions of layer activations specific to each domain through the implementation of the Local Maximum Mean Discrepancy (LMMD) method. The DSTLN consists of three modules: a feature extractor, fault category recognition, and domain adaptation. The feature extractor is constructed using a newly proposed SA-ConvLSTM model and CNNs, which aim to automatically learn features. The fault category recognition module is a classifier that categorizes the samples based on the extracted features. The domain adaptation module includes an adversarial domain classifier and subdomain distribution discrepancy metrics, making the learned features domain-invariant across both the global domain and subdomains. Through 210 transfer fault diagnosis experiments with wheel bearing data under 15 different operating conditions, the proposed method demonstrates its effectiveness

Anomaly Data Detection of Rolling Element Bearings Vibration Signal Based on Parameter Optimization Isolation Forest

Anomaly data detection is not only an important part of the condition monitoring process of rolling element bearings, but also the premise of data cleaning, compensation and mining. Aiming at the abnormal data segment detection of the vibration signals of a rolling element bearing, this paper proposes an abnormal data detection model based on comprehensive features and parameter optimization isolation forest (CF-POIF), which can adaptively identify abnormal data segments. First, in order to extract the mutation feature of vibration signals more accurately, the concept of comprehensive feature is proposed, which integrates the time domain and wavelet packet energy features. Then, the particle swarm optimization (PSO) algorithm is used to optimize the rectangular window length and sub sample set capacity in the isolation forest for anomaly detection. Finally, three real cases concerning abnormal data are used to verify the effectiveness of the proposed method. The results demonstrate that the proposed method is able to detect missing data, drift data and external interference data effectively, and it has a higher F1 score and accuracy compared to other methods