A comparative evaluation of neural networks and hidden Markov models for monitoring turning tool wear. - Cornie Scheffer; Herman A. Engelbrecht; P. Stephan Heyns (Neural Comput. Appl., 2005)

Principal Component Analysis and Automatic Relevance Determination in Damage Identification - L. Mdlazi; Tshilidzi Marwala; CJ Stander; Cornie Scheffer; P. Stephan Heyns (CoRR, 2007)

Using artificial intelligence for data reduction in mechanical engineering - L. Mdlazi; CJ Stander; P. Stephan Heyns; Tshilidzi Marwala (CoRR, 2007)

Machine and component residual life estimation through the application of neural networks. - M. A. Herzog; Tshilidzi Marwala; P. Stephan Heyns (Reliab. Eng. Syst. Saf., 2009)

The sensitivity of single objective optimization algorithm control parameter values under different computational constraints. - Antoine S. D. Dymond; Andries P. Engelbrecht; P. Stephan Heyns (IEEE Congress on Evolutionary Computation, 2011)

An empirical re-sampling method on intrinsic mode function to deal with speed variation in machine fault diagnostics. - Kesheng Wang; P. Stephan Heyns (Appl. Soft Comput., 2011)

Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties. - Sukhomay Pal; P. Stephan Heyns; Burkhard H. Freyer; Nico J. Theron; Surjya K. Pal (J. Intell. Manuf., 2011)

Consistent haul road condition monitoring by means of vehicle response normalisation with Gaussian processes. - T. Heyns; Johan Pieter de Villiers; P. Stephan Heyns (Eng. Appl. Artif. Intell., 2012)

The sensitivity of multi-objective optimization algorithm performance to objective function evaluation budgets. - Antoine S. D. Dymond; Schalk Kok; P. Stephan Heyns (IEEE Congress on Evolutionary Computation, 2013)

Comparing orthogonal force and unidirectional strain component processing for tool condition monitoring. - Burkhard H. Freyer; P. Stephan Heyns; Nico J. Theron (J. Intell. Manuf., 2014)

Acoustic Emission-Based Prognostics of Slow Rotating Bearing Using Bayesian Techniques Under Dependent and Independent Samples. - Sylvester A. Aye; P. Stephan Heyns (Appl. Artif. Intell., 2015)

Tuning Optimization Algorithms Under Multiple Objective Function Evaluation Budgets. - Antoine S. D. Dymond; Andries P. Engelbrecht; Schalk Kok; P. Stephan Heyns (IEEE Trans. Evol. Comput., 2015)

MOTA: A Many-Objective Tuning Algorithm Specialized for Tuning under Multiple Objective Function Evaluation Budgets. - Antoine S. D. Dymond; Schalk Kok; P. Stephan Heyns (Evol. Comput., 2017)

Prognostics of slow speed bearings using a composite integrated Gaussian process regression model. - Sylvester A. Aye; P. Stephan Heyns (Int. J. Prod. Res., 2018)

A Simple Condition Monitoring Method for Gearboxes Operating in Impulsive Environments. - Stephan Schmidt 005; Radoslaw Zimroz; Fakher Chaari; P. Stephan Heyns; Mohamed Haddar (Sensors, 2020)

Application of Anti-Diagonal Averaging in Response Reconstruction. - Bradley Dean Collins; P. Stephan Heyns; Schalk Kok; Daniel N. Wilke (Symmetry, 2021)

Towards a Deep Reinforcement Learning based approach for real time decision making and resource allocation for Prognostics and Health Management applications. - Ricardo Ludeke; P. Stephan Heyns (ICPHM, 2023)

A spectral regularisation framework for latent variable models designed for single channel applications. - Ryan Balshaw; P. Stephan Heyns; Daniel N. Wilke; Stephan Schmidt 005 (CoRR, 2023)

A framework to define, design and construct digital twins in the mining industry. - Luke van Eyk; P. Stephan Heyns (Comput. Ind. Eng., 2025)

Spectrally regularised LVMs: A spectral regularisation framework for latent variable models designed for single-channel applications. - Ryan Balshaw; P. Stephan Heyns; Daniel N. Wilke; Stephan Schmidt 005 (SoftwareX, 2025)