Quantum-enhanced LSTM for predictive maintenance in industrial IoT systems

Abstract

An innovative solution for predictive maintenance in IIoT systems combining quantum computing with the proficiency of LSTM neural networks is proposed by us. Our concept is guided by a hybrid quantum-classical architecture to facilitate quantum computing to exploit high-dimensional industrial sensor measurements while preserving crucial temporal relationships through particular quantum channels. Through the combination of the representational ingenuity of quantum circuits, along with the sequence-based modelling of classical LSTMs, QE-LSTM is uniquely positioned to handle complicated time series coming out of industrial sensors. At the heart of our methodology are the following unique elements:•A collaborative framework integrating quantum and classical technologies allowing for the quantum computer to manage the complex analysis of high dimensional sensor data in the industry.•Quantum channel designs were aimed at minimizing temporal dependencies in temporal series industrial measurements, thereby maximizing the quality of sequential analysis.•Under ODS hindcasting, QE-LSTM improved F1 by 4-5 percentage points on SECOM and reduced RMSE and NASA Score on C-MAPSS; trends were consistent on IMMD (Table 1, Table 2). [Table: see text] [Table: see text] In the application of failure detection of bearing, QE-LSTM improves F1 over classical baselines on SECOM by 4-5 pp, with similar gains on IMMD; results on C-MAPSS (RUL) show consistent reductions in RMSE and NASA score.

Keywords: Industrial Internet of Things; Predictive maintenance; Quantum long short-term memory networks.

Graphical abstract

Fig. 1

Binary failure prediction — F1 across datasets (C-MAPSS, IMMD, SECOM). QE-LSTM attains the best F1 on all three: C-MAPSS 89.8 % (+2.3 pp vs LSTM, +1.5 pp vs CNN-LSTM), IMMD 85.6 % (+2.9 pp, +1.7 pp), SECOM 84.9 % (+9.6 pp, +6.3 pp). Trend: largest gains on the high-dimensional SECOM dataset.

Fig. 2

C-MAPSS FD001 (RUL) — RMSE and NASA Score. QE-LSTM reduces RMSE to 18.1 (from 20.6; −12.1 %) and NASA Score to 603 (from 692; −12.8 %) versus LSTM (Table 2).

Fig. 3

C-MAPSS FD001 — observed vs predicted RUL (test set). QE-LSTM shows a tighter fit to the identity line and fewer large-error cases (>20 cycles) than LSTM/CNN-LSTM, consistent with the RMSE/Score gains in Table 2.

Fig. 4

IMMD (bearing failure) — early fault detection lead time. At failure probability 0.7, QE-LSTM triggers 105 h before failure vs 68 h (CNN-LSTM) and 42 h (LSTM), i.e., +63 h over LSTM. With the cost model in text, this maps to up to $214k savings per event at full utilization.

Fig. 5

SECOM — F1 improvement over LSTM vs. PCA feature dimensionality. Quantum gains increase with dimensionality: +1.3 % (50 dims) → +16.7 % (591 dims). Trend supports the hypothesis that QE features help most in high-dimensional regimes.