Objective  We completed a prospective human cadaveric study to determine the ability of a ball bearing (BB) pellet to penetrate the orbit and/or surrounding structures. Methods  A single trained sergeant officer discharged an alloy steel air rifle to eight cadaver orbits from four adult human cadaver heads. Five BB pellets each were aimed at three locations (caruncle, upper eyelid, or lower eyelid) at 10 cm and 1 m, and then less specifically, at the orbital region for 3- and 5-m distances. Computed tomography (CT) of the cadaver heads was performed. Final locations of BB pellets are divided into three categories: intracranial, surrounding orbital structures including the pterygopalatine fossa and infratemporal fossa, and orbit. Results  Of 40 BB pellets, 37 penetrated soft tissue and were visualized on CT: 19 (51%) rested in the intracranial space, 17 (46%) in surrounding orbital structures, and 1 (3%) within the orbit. The deepest position of a pellet was in the parietal lobe, and most superficial location anterior to the frontal bone. Pellets discharged from 1 m were more likely to rest in the intracranial space compared with those from 10 cm ( p  < 0.001), 3 m ( p  = 0.011), and 5 m ( p  = 0.004). The distance of discharge was associated with final pellet location ( p  = 0.001). Conclusion  BB guns should be considered dangerous and potentially deadly when aimed at the orbit. Although the thick calvarium can protect the intracranial space from BB penetration, the orbit may be a vulnerable entry point with relatively low resistance, allowing penetration of the intracranial and periorbital spaces.

Background and objective Sizing on digital films is important for implants and planning deformity correction. CT is the most accurate digital measurement method. We use a 1-inch ball bearing (cost: $1) to size our long-leg standing films (LLSFs) when planning deformity correction. In this study, we aimed to assess the accuracy of digital measurements calibrated by this method. Methods We conducted An IRB-approved study involving 25 patients having both an LLSF with a 1-inch ball bearing taped to the inner mid-thigh and a CT scanogram. The longest distance in the axial cut of the bilateral ankle, knee, and femoral heads of the CT images were compared to the same anatomic locations on LLSFs calibrated with the ball bearing using the online digital planning software DetroitBonesetter (DBS) and measurements from our Picture Archiving Communication Software (PACS). Five observers performed each measurement. Results The average measurement differences between the gold standard CT scan and LLSFs calibrated with DBS were as follows: 0.110 ± 0.432 mm (femoral head); 2.173 ± 0.0619 mm (knee); and 3.671 ± 0.30 mm (ankle). In PACS, they were as follows: 5.470 ± 0.381 mm (femoral head); 6.248 ± 0.712 mm (knee); and 1.806 ± 0.548 mm (ankle). The intraclass correlation coefficient for 600 measurements by five observers was 0.972. Conclusions The $1 ball-bearing sizing on DBS using LLSFs provides accuracy to <1 mm for the femoral head, 2 mm at the knee, and 3.7 mm at the ankle. It was significantly better than the PACS system for both the femoral head and knee (<0.001), while PACS was better at the ankle (<0.001).

This study focused on designing the raceway tolerance of high-precision rolling bearings. After the tolerance error equivalence relationship is defined, the application of the rolling bearing error equalization effect in tolerance design is studied. First, a five-degree of freedom quasi-static model was established for angular contact ball bearings. The waviness was included in the manufacturing error, and the radial and axial runouts of the bearing inner ring were calculated. Second, the error homogenization effect of the rolling bearing was studied, and the error homogenization coefficient was defined. The results of the study demonstrated that the bearing rotary accuracy was higher than the raceway error by an order of magnitude. Third, the manufacturing error range of each precision grade of the rolling bearing raceway was obtained by investigating errors of the equalization coefficient. Finally, the specific value of the raceway tolerance of P2 rolling bearings was obtained.

OBJECTIVE: Carbon microspheres have been shown to reduce friction and surface wear at relatively low speeds and high applied loads (i.e., within the boundary lubrication regime). We hypothesize that in dilute colloidal lubricating systems there is an interplay between the size of the carbon microspheres and the lubrication gap size, which determines the dominant lubricating mechanism of the system.
METHODS: A 60 wt% aqueous glycerol solution was used as the base lubricant and compared to various carbon particle-based lubricant formulations ranging in particle concentrations from 0.05 to 0.30 vol%. The tribological properties of the various lubricant formulations were tested on a pin-on-disk tribometer. A simplified Stribeck plot was produced to understand the changing mechanism of lubrication over a wide range of conditions.
RESULTS: The Stribeck curves show that the carbon microspheres assist lubrication by a rolling mechanism primarily in the boundary lubrication regime. A 0.20 vol% carbon-based lubricant formulation showed the best friction reduction compared to the base lubricant. Increasing speed increases the lubricating gap between the friction pair beyond the size of the particles, thereby nullifying the rolling mechanism of the particles. We introduce a modified specific film thickness parameter to determine the lubrication regime in a particle-lubricant system.

Rotating machines are often operated under various operating conditions. However, the characteristics of the data varies with their operating conditions. This article presents the time-series dataset, including vibration, acoustic, temperature, and driving current data of rotating machines under varying operating conditions. The dataset was acquired using four ceramic shear ICP based accelerometers, one microphone, two thermocouples, and three current transformer (CT) based on the international organization for standardization (ISO) standard. The conditions of the rotating machine consisted of normal, bearing faults (inner and outer races), shaft misalignment, and rotor unbalance with three different torque load conditions (0 Nm, 2 Nm, and 4 Nm). This article also reports the vibration and driving current dataset of a rolling element bearing under varying speed conditions (680 RPM to 2460 RPM). The established dataset can be used to verify newly developed state-of-the-art methods for fault diagnosis of rotating machines. Mendeley Data. DOI:10.17632/ztmf3m7h5x.6, DOI:10.17632/vxkj334rzv.7, DOI:10.17632/x3vhp8t6hg.7, DOI:10.17632/j8d8pfkvj2.7.

The bearing is an essential component of a rotating machine. Sudden failure of the bearing may cause an unwanted breakdown of the manufacturing plant. In this paper, an intelligent fault diagnosis technique was developed to diagnose various faults that occur in a deep groove ball bearing. An experimental setup was designed and developed to generate faulty data in various conditions, such as inner race fault, outer race fault, and cage fault, along with the healthy condition. The time waveform of raw vibration data generated from the system was transformed into a frequency spectrum using the fast Fourier transform (FFT) method. These FFT signals were analyzed to detect the defective bearing. Another significant contribution of this paper is the application of a machine learning (ML) algorithm to diagnose bearing faults. The support vector machine (SVM) was used as the primary algorithm. As the efficiency of SVM heavily depends on hyperparameter tuning and optimum feature selection, the particle swarm optimization (PSO) technique was used to improve the model performance. The classification accuracy obtained using SVM with a traditional grid search cross-validation (CV) optimizer was 92%, whereas the improved accuracy using the PSO-based SVM was found to be 93.9%. The developed model was also compared with other traditional ML techniques such as k-nearest neighbor (KNN), decision tree (DT), and linear discriminant analysis (LDA). In every case, the proposed model outperformed the existing algorithms.

Fault size estimation is of great importance to bearing performance degradation assessment and life prediction. Until now, fault size estimation has generally been based on acoustic emission signals or vibration signals; an approach based on current signals has not yet been mentioned. In the present research, an approximate estimation approach based on current is introduced. The proposed approach is easy to implement for existing inverter-driven induction motors without complicated calculations and additional sensors, immune to external disturbances, and suitable for harsh conditions. Firstly, a feature transmission route from spall, to Hertzian forces, and then to friction torque is simulated based on a spall model and dynamic model of the bearing. Based on simulated results, the relation between spall size and the multiple characteristic vibration frequencies in friction torque is revealed. Secondly, the multiple characteristic vibration frequencies modulated in the current is investigated. Analysis results show that those frequencies modulated in the current are independent of each other, without spectrum overlap. Thirdly, to address the issue of which fault features modulated in the current are very weak, a fault-feature-highlighting approach based on reduced voltage frequency ratio is proposed. Finally, experimental tests were conducted. The obtained results validate that the proposed approach is feasible and effective for spall size estimation.

The monitoring of rotating machinery condition has been a critical component of the Industry 4.0 revolution in enhancing machine reliability and facilitating intelligent manufacturing. The introduction of condition-based monitoring has effectively reduced the catastrophic events and maintenance cost across various industries. One of the major challenges of the diagnosis remains as majority of the diagnostic model requires off-line analysis and human intervention. The offline analysis, which is normally done by previous experience, involves tuning model parameters to improve the performance of the diagnostic model. However, for newly developed models, the knowledge of the unknown parameters does not exist. One way to resolve this issue is through learning using adaptation. The adaptation algorithm adjusts itself by newly acquired data. Hence, improvement of the model performance is achieved. In this paper, a nonlinear adaptive dictionary learning algorithm is proposed to achieve early fault detection of bearing elements without using the conventional computation heavy algorithm to update the dictionary. Deterministic and random data separation is implemented using the autoregressive model to reduce the background noise. The filtered data is further analyzed by the Infogram to reveal the impulsiveness and cyclostationary signature of the vibration signal. The dictionary is initialized using random parameters. Instead of using the k means singular value decomposition algorithm to compute the dictionary for adaptation, the unscented Kalman filter (UKF) is implemented to update the dictionaries using the filtered signal from the Infogram. The updating algorithm does not require computation of the dictionary, and no previous knowledge of the dictionary\'s parameters is needed. The updated dictionary contains the detected fault signature from the Infogram and, therefore, is used for further fault analysis. The proposed algorithm has the advantage of self-adaptation, the capability to map the non-linear relationship of the signal and dictionary weights. The algorithm can be used in the various condition-based monitoring of rotating machineries to avoid additional human efforts and improve the performance of the diagnostic model.

The successful prediction of the remaining useful life of rolling element bearings depends on the capability of early fault detection. A critical step in fault diagnosis is to use the correct signal processing techniques to extract the fault signal. This paper proposes a newly developed diagnostic model using a sparse-based empirical wavelet transform (EWT) to enhance the fault signal to noise ratio. The unprocessed signal is first analyzed using the kurtogram to locate the fault frequency band and filter out the system noise. Then, the preproc signal is filtered using the EWT. The l q -regularized sparse regression is implemented to obtain a sparse solution of the defect signal in the frequency domain. The proposed method demonstrates a significant improvement of the signal to noise ratio and is applicable for detection of cyclic fault, which includes the extraction of the fault signatures of bearings and gearboxes.

One of the most common parts to maintain system balance and support the various load in rotating machinery is the rolling element bearing. The breakdown of the element in bearings leads to inefficiency and sometimes catastrophic events across various industries. The main challenge over the last few years for fault diagnosis of bearings is the early detection of fault signature. In this paper, an adaptive online dictionary learning algorithm is developed for early fault detection of bearing elements. The dictionary is trained using a set of vibration signal from a heavily damaged bearing. The enveloped signal of the bearing is obtained using the Kurtogram and split into several sections. The K-SVD algorithm is implemented to the dictionaries corresponding to the enveloped signal. OMP is implemented with the calculated dictionaries to obtain the sparse representation of the testing signal. Then the envelope analysis is implemented to obtain the fault signal from the recovered signal by the dictionaries. The adaptive algorithm is added to the dictionary learning to update the dictionary based on newly acquired data with the weighted least square method. Without retraining the dictionaries using the K-SVD algorithm, the computation speed is significantly improved. The proposed algorithm is compared with a traditional dictionary learning algorithm to show the improvement in detection of new fault frequency and improved signal to noise ratio.