Table 2 Key literature of real... | Emerald Publishing

Table 2

Key literature of real applications of PdM platforms within I4.0

Ref	Approach	Pros	Cons
Rousopoulou et al. (2022)	Platform based on LSTM	Advanced real-time visualisation and user interface Live monitoring and early prediction of machine features	Limited to anomaly detection and time-series forecasting No statistical and prescriptive analytics No HMC integration
Wang et al. (2023)	Platform based on KNN-LSTM	Well-structured, comprehensive work High accuracy Maintenance scheme recommendation Operating and failure rates, MTTR, MTBF Real-time visualisation and interface	High training costs and long evaluation time for large datasets Lacks OEE assessment No HMC integration
Mohan et al. (2023)	Platform based on LSTM	RUL prediction Linked to Total Productive Maintenance Includes OEE assessment Multi-device access and sdvanced visualisation	Critical equipment only, with data sampled every 3 min and predictions every 192 min No probabilistic failure assessment No HMC integration

Ref	Approach	Pros	Cons
Rousopoulou et al. (2022)	Platform based on LSTM	Advanced real-time visualisation and user interface Live monitoring and early prediction of machine features	Limited to anomaly detection and time-series forecasting No statistical and prescriptive analytics No HMC integration
Wang et al. (2023)	Platform based on KNN-LSTM	Well-structured, comprehensive work High accuracy Maintenance scheme recommendation Operating and failure rates, MTTR, MTBF Real-time visualisation and interface	High training costs and long evaluation time for large datasets Lacks OEE assessment No HMC integration
Mohan et al. (2023)	Platform based on LSTM	RUL prediction Linked to Total Productive Maintenance Includes OEE assessment Multi-device access and sdvanced visualisation	Critical equipment only, with data sampled every 3 min and predictions every 192 min No probabilistic failure assessment No HMC integration

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