Mechanical Systems and Signal Processing (MSSP) is an interdisciplinary journal in Mechanical, Aerospace and Civil Engineering with the purpose of reporting scientific advancements of the highest quality arising from new techniques in sensing, instrumentation, signal processing, modelling and control of dynamic systems. MSSP papers are expected to make a demonstrable original contribution to engineering knowledge, which should be significant in terms of advancement over established methods. Especially sought are papers that include both theoretical and experimental aspects, or that include theoretical material of high relevance to practical applications. MSSP is a leader in its field and research areas covered include:

1. Actuation, Sensing and Control

• Vibration & noise control
• Travelling waves
• Smart-material systems
• Piezoelectrics
• Adaptivity
• Integrated systems

2. Measurement & Signal Processing

• Signal processing for the understanding of mechanical systems
• Full-field vibration/acoustic measurements
• Big data problems

3. Nonlinearity

• Nonlinear vibration problems
• Nonlinear normal modes
• Energy harvesting

4. Rotating Machines, Machinery Diagnostics & SHM

• Diagnostics and prognostics
• Rotor dynamics
• Cracks in rotors
• Bearings and gears

5. Uncertainty Quantification

• Probabilistic, interval & fuzzy analysis
• Reliability and robustness
• Bayesian methods

6. Vibrations, Modal Analysis & Structures

• Structural modelling & identification
• Inverse problems
• Operational modal analysis
• Ambient vibration testing

Authors of papers with Machine-Learning or Signal Processing content should see the MSSP guidelines on these subjects: http://media.journals.elsevier.com/content/files/machine-learning-04180327.pdf
https://www.elsevier.com/__data/promis_misc/SignalProcessing.pdf 

Special Issue on Intelligent Techniques for Real-time Signal Processing and Mechanical Systems Diagnosis- New Directions, Challenges and Applications
Submission Date: 2019-12-29

By monitoring the energy coming from mechanical systems (e.g., acoustics and vibration emission), it is possible to estimate not only actual condition but also future behavior of the machine. Problems involved on diagnosis via acoustics and vibration monitoring reside in time-varying nature of measured signals, complexity of the vibration pattern of defective mechanical components, interference of random signals and sources of acoustics and vibration emission, and so forth.

We are currently living through the fourth Industrial revolution, which is riding on the wave of cutting-edge technologies in computing, artificial intelligence, and communications. The past decade has witnessed incredible advances in the field of artificial intelligence (AI) and has seen massive proliferation of cloud computing technologies. These technological advances have further fueled the integration of the real-time cyber and the physical worlds, with intelligence and autonomy as its key hallmarks, which would lead to more reliable, productive, and efficient industries and businesses in the future.

Intelligent techniques applied on real-time machine condition monitoring can be classified into:

Preprocessing techniques (for signal conditioning, such as filtering and deconvolution techniques, genetic algorithms applications, etc.)

Feature extraction techniques (temporal and spectral analysis, envelope detection, higher-order statistical and cyclostationary processing, time-frequency analysis)

Condition classification techniques (artificial neural network applications, expert systems, fuzzy logic, etc.)

Spectral analysis emerges as the signal processing technique more used for machine fault detection. However, nonlinearity, and nonstationarity properties of acoustics and real-time vibration signal emitted by certain mechanical components, and the challenge of estimating low-magnitude signal properties at noise environments, have led to the application of advanced signal processing techniques such as time-frequency analysis, higher-order statistical processing, cyclostationary analysis.

Recognizing the growing importance of and interest in effective application of real-time signal processing techniques on machine diagnosis, Advances in Acoustics and Vibration will devote a special issue to innovative research papers in advanced acoustics and vibration analysis for machine condition monitoring.

For example, SCADA (Supervisory control and data acquisition) systems are network presence systems that face significant threats and attacks. After an attack occurred, SCADA requires forensic investigation to understand the cause and effects of the intrusion or disruption on the system’s services. However, forensic investigators cannot turn it off during acquiring the real-time data that is required for the investigation and analysis process. That is because the system’s services need to be continuously operational. Despite the great efforts to acquire live data on SCADA systems, the continuously change of this type of data and the risk on the system’s services make it a big challenge. Intelligent techniques for Real-time Signal Processing and Mechanical Systems Diagnosis are urgent in such cases to predict and prevent SCADA failures.

We invite researchers and practicing engineers to contribute original research articles that discuss issues related but not limited to:

Condition-based real-time monitoring, real-time fault diagnosis and prognosis of industrial machines and mechanical structures,

Intelligent real-time diagnostic and prognostic techniques for industrial applications. These techniques include deep learning, transfer learning, and neuro-fuzzy inference techniques,

AI-based solutions that are explainable, solutions utilizing the Industrial Internet. of Things, cloud computing, cyber physical systems, and machine-to-machine interfaces and paradigms for fault diagnosis and prognosis in the context of Industry 4.0.

Smart real-time data acquisition and signal processing in industrial systems, such as SCADA

Future research directions of Industrial Internet of Things towards the fifth industrial revolution.

We would also welcome review articles that capture the current state-of-the art and outline future areas of research in the fields relevant to this Special Issue.

Before submission, authors should carefully read the journal’s author guidelines, which are located at https://www.elsevier.com/journals/mechanical-systems-and-signal-processing/0888-3270/guide-for-authors. Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at https://ees.elsevier.com/ymssp/default.asp?pg=login.asp according to the following timetable: