Aims

Mechanical Systems Reliability and Diagnostics (MSRD) is an international, peer-reviewed, open-access journal devoted to the study of reliability, degradation, monitoring, and diagnostic processes in mechanical systems operating under practical and industrial conditions. The journal publishes research that examines how mechanical systems evolve in service, how failure mechanisms develop and interact, and how system health can be assessed, interpreted, and managed through analytical, experimental, and data-informed approaches.

The journal considers mechanical systems as operational entities whose behaviour changes over time under load, wear, environmental exposure, and varying usage conditions. Emphasis is placed on system-level analysis of degradation behaviour, failure propagation, diagnostic interpretation, and maintenance decision processes across the life cycle of engineered systems.

Particular attention is given to the analytical foundations of reliability and diagnostics. This includes modelling of degradation and failure processes, structured interpretation of monitoring signals, integration of measurements with physical system understanding, and formulation of maintenance and life-cycle decisions grounded in observable system evidence. Submissions are expected to establish a clear link between mechanical behaviour, measurable indicators, and reliability inference, rather than presenting algorithmic refinements or descriptive case material without analytical depth.

The journal supports research that improves understanding of how mechanical systems can be monitored and managed to sustain dependable operation under uncertainty. Studies combining experimental validation, transparent modelling assumptions, and reproducible diagnostic reasoning are especially encouraged. Clarity of system definition, traceability of inference, and robustness of conclusions are central evaluation criteria.

MSRD welcomes conceptual, methodological, computational, and experimentally grounded contributions that advance systematic understanding of failure, health, and operational integrity in mechanical systems. The journal is published quarterly by Acadlore and follows a structured peer-review process designed to ensure methodological transparency, consistency of evaluation, and analytical rigour.

Key features of MSRD include:

• Focuses on mechanical systems as evolving operational systems rather than isolated components or purely theoretical abstractions;

• Emphasises reliability, degradation behaviour, and diagnostic reasoning supported by measurable system response and engineering evidence;

• Requires explicit analytical, experimental, or modelling frameworks linking system behaviour to reliability assessment or diagnostic interpretation;

• Encourages work on fault detection, degradation modelling, and life prediction within a clearly defined system context;

• Values integration of monitoring data with physical modelling, statistical reasoning, or structured diagnostic logic;

• Supports comparative and validation-oriented studies where methods are assessed against experimental or field evidence;

• Maintains editorial standards prioritising transparency of assumptions, reproducibility of analysis, and coherence between observations and conclusions.

Scope

MSRD invites original research articles, theoretical contributions, systematic reviews, and well-documented empirical or computational studies that provide analytical insight into reliability and diagnostic processes in mechanical systems. Areas of interest include, but are not limited to, the following:

Mechanical Systems and Operational Behaviour

• Rotating machinery, transmission systems, fluid-power systems, automotive and energy applications

• Structural and dynamic behaviour of mechanical assemblies

• Load-dependent performance variability

• System response under variable operating conditions

• Interaction between components and system-level performance

Reliability, Degradation, and Life Prediction

• Modelling of failure and degradation processes

• Fatigue, wear, fracture, and creep mechanisms in mechanical systems

• Remaining useful life (RUL) estimation

• Probabilistic and statistical reliability analysis

• Life-cycle performance modelling under uncertainty

Diagnostics and Health Monitoring

• Fault detection and isolation in mechanical systems

• Condition monitoring using vibration, acoustic, thermal, or multi-sensor data

• Evidence-based diagnostic reasoning

• Integration of experimental measurements with reliability inference

• Diagnostic validation and benchmarking

Experimental Methods and Validation

• Experimental characterisation of degradation and failure mechanisms

• Measurement techniques for system health assessment

• Uncertainty quantification in experimental diagnostics

• Comparison between analytical predictions and field data

• Reproducibility and robustness of diagnostic methodologies

Data-Informed and Hybrid Approaches

• Data-driven methods for system health assessment

• Hybrid physical–statistical modelling frameworks

• Inference under incomplete or noisy monitoring data

• Structured interpretation of diagnostic indicators

• Integration of monitoring data into reliability decision processes

Maintenance, Risk, and Operational Decision-Making

• Risk-informed maintenance strategies

• Condition-based and predictive maintenance frameworks

• Maintenance optimisation under uncertainty

• Decision support based on reliability and diagnostic evidence

• Operational planning for mechanical system integrity

Evaluation and Methodological Reflection

• Validation of diagnostic and reliability models

• Sensitivity and robustness analysis

• Comparative assessment of monitoring and inference approaches

• Critical examination of modelling assumptions

• Interpretability and transparency in diagnostic reasoning

Related and Emerging Topics

• Digital representations of mechanical system health

• Multi-scale degradation modelling

• System reliability in complex industrial environments

• Longitudinal monitoring and dynamic performance analysis

• Integration of mechanical diagnostics with broader operational systems