In mechanical engineering field, sealing technology is of vital importance for the success of a product. A poor sealing design of a slipper-swash plate friction pair (SSPFP) could lower the reliability of a hydraulic piston pump and lead to the failure of the pump. In this paper, a reliability-based robust design method for the sealing of the SSPFP in hydraulic piston pumps is first established. The matrix expression of the relation between the numerical characteristics of the state function of the SSPFP and the random variables is derived. Methods for calculating the sealing reliability and analyzing the reliability sensitivity of the SSPFP in the hydraulic piston pump are provided. Then, the reliability-based robust design is formulated as a multiobjective design optimization problem to meet the reliability requirements. A numerical example is presented to demonstrate performance of the proposed sealing reliability-based robust design method.

In the age of Internet of Things and Industrial 4.0, prognostic and health management (PHM) systems are used to collect massive real-time data from mechanical equipment. PHM big data has the characteristics of large-volume, diversity, and high-velocity. Effectively mining features from such data and accurately predicting the remaining useful life (RUL) of the rotating components with new advanced methods become issues in PHM. Traditional data driven prognostics is based on shallow learning architectures, requires establishing explicit model equations and much prior knowledge about signal processing techniques and prognostic expertise, and therefore is limited in the age of big data. This paper presents a deep learning-based approach for RUL prediction of rotating components with big data. The presented approach is tested and validated using data collected from a gear test rig and bearing run-to-failure tests and compared with existing PHM methods. The test results show the promising RUL prediction performance of the deep learning-based approach.

Judgment analysis (JA) is a technique for modeling and interpreting human judgments that is usually based on multiple linear regression. However, the linear assumptions inherent to this approach can be limiting for modeling both the human judgments and the environmental criterion. This paper addresses this by introducing a formulation of JA based on large memory storage and retrieval (LAMSTAR) artificial neural networks. We describe our LAMSTAR network JA process and use it to analyze data from an air traffic control conflict prediction task. These results are compared with those of a traditional regression-based lens model analysis. We found that the LAMSTAR-based JA did a better job of capturing human judgment, while the regression-based model was more appropriate for the criterion. This suggest that the LAMSTAR-based JA approach has utility when human judgments are not well represented by a linear model. We discuss our results with respect to both the specific application we evaluated as well as meta-analyses of the JA literature. We also explore avenues for future research.

In comparison with a traditional planetary gearbox, the split torque gearbox (STG) potentially offers lower weight, increased reliability, and improved efficiency. These benefits have driven helicopter object exchange models (OEMs) to develop products using STG. However, the unique structure of the STG creates a problem on how to locate the gear faults in an STG. As of today, only limited research on STG fault detection using vibration and acoustic emission (AE) sensors has been conducted. In this paper, an effective gear fault location detection methodology using AE sensors for STG is presented. The methodology uses wavelet transform to process AE sensor signals at different locations to determine the arrival time of the AE bursts. By analyzing the arrival time of the AE bursts, the gear fault location can be determined. The parameters of the wavelets are optimized by using an ant colony optimization algorithm. Real seeded gear fault experimental tests on a notional STG are conducted. AE signals at different locations of the gearbox with both healthy and damaged output driving gears are collected simultaneously to determine the location of the damaged gear. Experimental results have shown the effectiveness of the presented methodology.

Full ceramic bearings are considered the first step toward full ceramic, oil-free engines in the future. No research on full ceramic bearing fault diagnostics using acoustic emission (AE) sensors has been reported. Unlike their steel counterparts, signal processing methods to extract effective AE fault characteristic features and fault diagnostic systems for full ceramic bearings have not been developed. In this paper, a data mining based full ceramic bearing diagnostic system using AE based condition indicators (CIs) is presented. The system utilizes a new signal processing method based on Hilbert Huang transform to extract AE fault features for the computation of CIs. These CIs are used to build a data mining based fault classifier using a k-nearest neighbor algorithm. Seeded fault tests on full ceramic bearing outer race, inner race, balls, and cage are conducted on a bearing diagnostic test rig and AE burst data are collected. The effectiveness of the developed fault diagnostic system is validated using real full ceramic bearing seeded fault test data.

Low-complexity, repetitive protein sequences with a limited amino acid palette are abundant in nature, and many of them play an important role in the structure and function of certain types of proteins. However, such repetitive sequences often do not have rigidly defined motifs. Consequently, the identification of these low-complexity repetitive elements has proven challenging for existing pattern-matching algorithms. Here we introduce a new web-tool SubSeqer (http://compsysbio.org/subseqer/) which uses graphical visualization methods borrowed from protein interaction studies to identify and characterize repetitive elements in low-complexity sequences. Given their abundance, we suggest that SubSeqer represents a valuable resource for the study of typically neglected low-complexity sequences.

Wyner-Ziv coding has recently emerged as an alternative to conventional DPCM coding for compression of sources with memory, particularly in video compression. This paper studies the operational rate-distortion performance of Wyner-Ziv coding, using uniform scalar quantization followed by perfect Slepian-Wolf coding, for compression of a Laplace-Markov source. The performance gap of this technique relative to DPCM coding is characterized through derived rate-distortion expressions and numerical simulations.

In this paper, a novel discriminant analysis based predictive model for preventing false alarms leading to unnecessary replacement of an avionic system component is presented. The model is validated by prediction of false alarms (also known as false positives, type I, or alpha errors) in the left generator shaft of a Sikorsky helicopter UH-60, using the Goodrich health and usage management system (HUMS). The paper presents one of the first approaches based on applying discriminant analysis for prognostics of avionic systems, specifically in the context of identifying false positives within the next 1 or 2 h. In practice, predictions for the next 2 h are sufficient as typical helicopter flight schedules and durations are such that up to 2 h advance notice is most useful. This is an important contribution because drive train components of helicopters are normally very robust with very rare failures; therefore, the cost of unnecessary preventive maintenance based on false alarms is very high.

Assessment of reliability and safety of a complex system with sequential failures is an important issue in industries, since the reliability and safety of a complex system depends, not only upon all failed states of the system components, but also upon the sequence of occurrence of those failures. In this paper, we present an alternative methodology for assessing sequential failures in a complex system. In previous research, the probability of sequential failures is computed based on tracking the markings of Petri net models and it is assumed that the time to failure of basic events follows an exponential distribution. To overcome the limitations of the current methods in applying Petri nets for assessment of sequential failures, the method presented in this paper employs the concept of counters used in Petri net simulation to perform sequential failure analysis and is developed based on evolution equations that describe the dynamics of the system. It is demonstrated with a sequential failure analysis example of an automated machining and assembly system.

Petri net is a powerful technique widely used in modeling and analysis of complex systems and processes such as manufacturing systems and nuclear power plants. Due to its capability in modeling the dynamics of the systems, Petri net has been combined with fault tree analysis techniques to determine the failure rates of the systems. Although a promising approach, current methods in combining Petri net with fault tree analysis for determining system failure rates assume that the failure rates of the basic events in a system are the same and the Petri net model of the system consists of simple structures. These assumptions do not reflect the real industrial applications for system failure analysis in an agile manufacturing environment. To overcome the limitations of the current methods in applying Petri net for failure and safety assessment of systems, this paper extends the current methods to real applications where basic events can have different failure rates and the systems can be modeled with complex Petri net structures. An example of failure analysis of a robotic manufacturing cell is provided to demonstrate the developed methods.

To respond to the challenge of agile manufacturing, companies are striving to provide a large variety of products at a low cost. Product modularity allows production of different products by combining standard components. One of the characteristics of modular products is that they share the same assembly operations for a part of their structure. The special structure of modular products provides challenges and opportunities for the design of assembly systems. Given a family of modular products, designing low cost assembly systems is an important problem. In this paper, an approach for the design of assembly systems for modular products is proposed. The assembly system is decomposed into two subsystems based on the structure of modular products. The configuration problem of the assembly system is formulated and solved by a tabu search based algorithm.