Mechanical Properties and Deterioration Instability of Filling Body with Gangue-Cement Material Based on AE Experiment

Gangue materials have been used to solve mine disasters with a support tunnel along the goaf and filling mining. Mastering the properties and damage characteristics of filling materials is an important basis for effective implementation. Based on the conventional uniaxial compression acoustic emission (AE) test, the effects of cementitious materials, ratio between water and cementitious material, gangue particle size, and grading parameters on the mechanical properties of gangue-cement samples were analyzed. The stage characteristics of compression deformation were studied. The fracture propagation characteristics and rock mass failure types induced by different graded gangues were revealed. The fracture forming mechanism from clustered damage and failure was interpreted. The results show that the compressive strength of the backfill increases with the increase of cementitious material; however, it decreases with the increase of water binder ratio. Controlling the proportion and dosage of materials was the key factor to realizing pumpability and stability. Combined with the deformation and AE characteristics, the failure stage of the backfill body is divided into three stages: linear deformation-low energy changing, block compression-high energy changing, and gentle stability-stable energy changing. Affected by the gangue distribution, the load in each stage will induce fracture to produce five distribution modes of single, turning, breakthrough, bifurcated, and collapsed surrounding gangue. In the process of loading failure, different gradation and particle sizes will also change its stress concentration characteristics, resulting in the transformation of rock failure types. The surface structure and roughness of gangue play an important role in the compressive performance of cement paste. The research results try to provide some guidance for efficient filling mining.

Evaluation of Impact of the Operational and Technical Factors on Downtime of Municipal Buses Based on a Linear Regression Model

The objective of this study was to assess the effect of selected operational and technical factors on downtime of vehicles. The sample consisted of buses from a municipal transport company (Poland). Estimation of parameters of a linear regression model was performed. Month of failure (downtime event) and its type were used as predictors. Failures were divided into three categories: events related to the company’s operations, including vehicle failures (1) and other (organizational) problems (2), as well as failures caused by external factors unrelated to the operations of the transport company (3). The downtime was found to be significantly associated with failure type and month of failure. A linear regression model of downtime with a reduced number of impact factors, taking into account two main failure types and two main periods of their occurrence during the year, was developed.

Competitive Failure of Loosening and Fatigue of Bolts under Composite Excitation

At present, research on the loosening of bolts under transverse excitation and their fatigue under axial excitation has been relatively mature, but research on the competitive relationship and failure characteristics between loosening and fatigue of bolts under transverse and axial composite excitation is still insufficient. Therefore, a method to accurately determine the failure types of bolts is proposed in this study by conducting a competitive failure test of loosening and fatigue under composite excitation. According to this method, the failure types of bolts can be distinguished. The analysis results reveal that there is an obvious competitive failure relationship between the loosening and fatigue of bolts, and the failure mode is mainly affected by the ratio of the transverse and axial loads (ξ). There is a critical ξ of bolt-loosening or fatigue failure, and the critical ξ is an inherent property of the bolt and is unrelated to the load. The critical ξ of 8.8 grade M8 × 1.25 × 70 high-strength bolts under composite excitation is obtained as 0.075 mm/kN. The failure mode of bolts under composite excitation can be predicted based on the critical ξ.

Machine Learning based Identification and Classification of Field-Operation caused Solar Panel Failures observed in Electroluminescence Images

Failure or degradation effects lead to power losses in solar panels during their field operation and are identified commonly by electroluminescence imaging. Failures like potential induced degradation and light and enhanced temperature induced degradation require an identification of the electroluminescence pattern over the entire solar panel. As the manual process of analysing patterns is prone to error, we seek for an automatic detection of these failure types. We predict automatically the failure types potential induced degradation and light and enhanced temperature induced degradation by adopting the principle component analysis method in combination with a k-nearest neighbour classifier.<br>

Machine Learning based Identification and Classification of Field-Operation caused Solar Panel Failures observed in Electroluminescence Images

Failure or degradation effects lead to power losses in solar panels during their field operation and are identified commonly by electroluminescence imaging. Failures like potential induced degradation and light and enhanced temperature induced degradation require an identification of the electroluminescence pattern over the entire solar panel. As the manual process of analysing patterns is prone to error, we seek for an automatic detection of these failure types. We predict automatically the failure types potential induced degradation and light and enhanced temperature induced degradation by adopting the principle component analysis method in combination with a k-nearest neighbour classifier.<br>

Remaining useful life prediction of bearings with different failure types based on multi-feature and deep convolution transfer learning

The accurate prediction of the remaining useful life (RUL) of rolling bearings is of immense importance in ensuring the safe and smooth operation of machinery and equipment. Although the prediction accuracy has been improved by a predictive model based on deep learning, it is still limited in engineering because lots of models use single-scale features to predict and assume that the degradation data of each bearing has a consistent distribution. In this paper, A deep convolutional migration network based on spatial pyramid pooling (SPP-CNNTL) is proposed to obtain higher prediction accuracy with self-extraction of multi-feature from the original vibrating signal. And to consider the differences of the data distribution in different failure types, transfer learning (TL) added with maximum mean difference (MMD) measurement function is used in the RUL prediction part. Finally, the data of IEEE PHM 2012 Challenge is used for verification, and the results show that the method in this paper has high prediction accuracy.

Community-based wheelchair caster failures call for improvements in quality and increased frequency of preventative maintenance

Study design Secondary data analysis of wheelchair failures and service repair logs from a network of wheelchair suppliers. Objective To determine the frequency of wheelchair caster failures and service repairs across wheelchair manufacturers and models and investigate the relationships between them. Setting Wheelchair caster failures and service repairs occurred in the community. Methods Reported caster failure types were classified based on the risk they pose for user injuries and wheelchair damage. Caster failures experienced by users of tilt-in-space and ultralightweight manual wheelchair models and Group 2, 3 and 4 power wheelchair models between January 2017 and October 2019 were analyzed using Chi-Square tests for independence. Correlational analysis of failures and service repairs was performed. Results A total of 6470 failures and 151 service repairs reported across four manufacturers and five models were analyzed. Failure types were significantly associated with manufacturers and models, respectively. Users of tilt-in-space wheelchairs, who require greater seating support, experienced twice the proportion of high-risk caster failures than the ultralightweight manual wheelchair users. Similarly, Group 3 and 4 power wheelchair users, who have complex rehabilitation needs, experienced 15-36% more high-risk failures than Group 2 users. Service repairs negatively correlated with high-risk manual wheelchair caster failures. Conclusions Wheelchair users who have greater seating and complex rehabilitation needs are at a higher risk for sustaining injuries and secondary health complications due to frequent caster failures. The study findings call for significant reforms in product quality and preventative maintenance practices that can reduce wheelchair failures and user consequences.

A Vibration Feature Extraction Method Based on Time-Domain Dimensional Parameters and Mahalanobis Distance

To accurately describe the characteristics of a signal, the feature parameters in time domain and frequency domain are usually extracted for characterization. However, the total number of feature parameters in time domain and frequency domain exceeds twenty, and all of the feature parameters are used for feature extraction, which will result in a large amount of data processing. For the purpose of using fewer feature parameters to accurately reflect the characteristics of the vibration signal, a simple but effective vibration feature extraction method combining time-domain dimensional parameters (TDDP) and Mahalanobis distance (MD) is proposed, i.e., TDDP-MD. In this method, ten time-domain dimensional parameters are selected to extract fault features, and the distance evaluation technique based on Mahalanobis distance criterion function is also introduced to calculate the feature vector, which can be used to classify different failure types. Finally, the proposed method is applied to fault diagnosis of rolling element bearings, and experimental analysis results show that the proposed method can recognize different failure types accurately and effectively with only ten time-domain dimensional parameters and a small quantity of training samples.

Adhesion of Resin-Resin and Resin–Lithium Disilicate Ceramic: A Methodological Assessment

The aim of this study was to evaluate four test methods on the adhesion of resin composite to resin composite, and resin composite to glass ceramic. Resin composite specimens (N = 180, Quadrant Universal LC) were obtained and distributed randomly to test the adhesion of resin composite material and to ceramic materials (IPS e.max CAD) using one of the four following tests: (a) Macroshear SBT: (n = 30), (b) macrotensile TBT: (n = 30), (c) microshear µSBT: (n = 30) and (d) microtensile µTBT test (n = 6, composite-composite:216 sticks, ceramic-composite:216 sticks). Bonded specimens were stored for 24 h at 23 °C. Bond strength values were measured using a universal testing machine (1 mm/min), and failure types were analysed after debonding. Data were analysed using Univariate and Tukey’s, Bonneferroni post hoc test (α = 0.05). Two-parameter Weibull modulus, scale (m), and shape (0) were calculated. Test method and substrate type significantly affected the bond strength results, as well as their interaction term (p < 0.05). Resin composite to resin composite adhesion using SBT (24.4 ± 5)a, TBT (16.1 ± 4.4)b and µSBT (20.6 ± 7.4)a,b test methods presented significantly lower mean bond values (MPa), compared to µTBT (36.7 ± 8.9)b (p < 0.05). When testing adhesion of glass ceramics to resin composite, µSBT (6.6 ± 1)B showed the lowest and µTBT (24.8 ± 7)C,D the highest test values (MPa) (SBT (14.6 ± 5)A,D and TBT (19.9 ± 5)A,B) (p < 0.05). Resin composite adhesion to ceramic vs. resin composite did show significant difference for the test methods SBT and µTBT (resin composite (24.4 ± 5; 36.7 ± 9 MPa) vs. glass ceramic (14.6 ± 5; 25 ± 7 MPa)) (p > 0.05). Among substrate–test combinations, Weibull distribution presented the highest shape values for ceramic–resin in µSBT (7.6) and resin–resin in µSBT (5.7). Cohesive failures in resin–resin bond were most frequently observed in SBT (87%), followed by TBT (50%) and µSBT (50%), while mixed failures occurred mostly in ceramic–resin bonds in the SBT (100%), TBT (90%), and µSBT (90%) test types. According to Weibull modulus, failure types, and bond strength, µTBT tests might be more reliable for testing resin-based composites adhesion to resin, while µSBT might be more suitable for adhesion testing of resin-based composites to ceramic materials.