A Comprehensive Health Indicator Integrated by the Dynamic Risk Profile from Condition Monitoring Data and the Function of Financial Losses

Large rotating machinery, such as centrifugal gas compressors and pumps, have been widely applied and acted as crucial components in the oil and gas industries. Breakdowns or deteriorated performance of these rotating machines can bring significant economic loss to the companies. In order to conduct effective maintenance and avoid unplanned downtime, a system-wide health indicator is proposed in this paper. The health indicator not only uses a dynamic risk profile, but also considers financial loss and the fault probability based on condition monitoring data. This methodology is carried out by four steps: fault detection, probability of fault calculation, consequence of fault calculation and dynamic risk assessment. In our methodology, the fault probability is calculated by robust Mahalanobis distance, presenting as a system-wide feature from a sparse autoencoder fault detection model enabled early fault detection. The value of the health indicator is presented in financial loss, which assists in effective operational decision-making in a process system. To evaluate the performance of the proposed indicator, two case studies were carried out—one case tested on multivariate industrial data obtained from a pump, and another one tested on an industrial data set from a compressor. Results prove that the integrated health indicator can detect the faults at their incipient stages, indicate the degradation of the system with dynamically updated process risk at each sampling instant, and suggest an appropriate shutdown time before the system suffers severe damage. In addition, this methodology can be adapted to other machines’ health assessments, such as those of turbines and motors. The presented method of processing the industrial data set can benefit relevant readers.

Fault Injection as an Oscilloscope: Fault Correlation Analysis

Fault Injection (FI) attacks have become a practical threat to modern cryptographic implementations. Such attacks have recently focused more on exploitation of implementation-centric and device-specific properties of the faults. In this paper, we consider the parallel between SCA attacks and FI attacks; specifically, that many FI attacks rely on the data-dependency of activation and propagation of a fault, and SCA attacks similarly rely on data-dependent power usage. In fact, these are so closely related that we show that existing SCA attacks can be directly applied in a purely FI setting, by translating power FI results to generate FI ‘probability traces’ as an analogue of power traces. We impose only the requirements of the equivalent SCA attack (e.g., knowledge of the input plaintext for CPA on the first round), along with a way to observe the status of the target (whether or not it has failed and been “muted” after a fault). We also analyse existing attacks such as Fault Template Analysis in the light of this parallel, and discuss the limitations of our methodology. To demonstrate that our attacks are practical, we first show that SPA can be used to recover RSA private exponents using FI attacks. Subsequently, we show the generic nature of our attacks by performing DPA on AES after applying FI attacks to several different targets (with AVR, 32-bit ARM and RISC-V CPUs), using different software on each target, and do so with a low-cost (i.e., less than $50) power fault injection setup. We call this technique Fault Correlation Analysis (FCA), since we perform CPA on fault probability traces. To show that this technique is not limited to software, we also present FCA results against the hardware AES engine supported by one of our targets. Our results show that even without access to the ciphertext (e.g., where an FI redundancy countermeasure is in place, or where ciphertext is simply not exposed to an attacker in any circumstance) and in the presence of light jitter, FCA attacks can successfully recover keys on each of these targets.

Improved Hardness and Thermal Stability of Nanocrystalline Nickel Electrodeposited with the Addition of Cysteine

Experiments were conducted for the study of the effect of cysteine addition on the microstructure of nanocrystalline Ni films electrodeposited from a nickel sulfate-based bath. Furthermore, the thermal stability of the nanostructure of Ni layers processed with cysteine addition was also investigated. It was found that with increasing cysteine content in the bath, the grain size decreased, while the dislocation density and the twin fault probability increased. Simultaneously, the hardness increased due to cysteine addition through various effects. Saturation in the microstructure and hardness was achieved at cysteine contents of 0.3–0.4 g/L. Moreover, the texture changed from (220) to (200) with increasing the concentration of cysteine. The hardness of the Ni films processed with the addition of 0.4 g/L cysteine (∼6800 MPa) was higher than the values obtained for other additives in the literature (<6000 MPa). This hardness was further enhanced to ∼8400 MPa when the Ni film was heated up to 500 K. It was revealed that the hardness remained as high as 6000 MPa even after heating up to 750 K, while for other additives, the hardness decreased below 3000 MPa at the same temperature.