def main_motor_temperature_detect(self, df, para):
"""
主电机系统温度异常判断规则
"""
if not df.empty:
df = df.resample('T').mean()
dT = df['temp_out'] - df['temp_in']
# 进出口温升小于*，同时三项温度大于* => 冷却系统效果不好
if np.any((dT < para[0])
& ((df['A'] > para[1]) | (df['B'] > para[1])
| (df['C'] > para[1]))):
event = Event({
'assetid': self.graph.deviceid,
'meastime': df.index[0],
'level': 1,
'info': '冷却系统进出口温度异常！'
})
self.graph.events.append(event)
# 三项温度温差大于*，且绝对温度大于* => 定子温度异常
if np.any((((df['A'] - df['B']) > para[2]) & (
(df['A'] > para[1]) & (df['B'] > para[1])))
| (((df['A'] - df['C']) > para[3]) & (
(df['A'] > para[1]) & (df['C'] > para[1])))
| (((df['B'] - df['C']) > para[4]) & (
(df['B'] > para[1]) & (df['C'] > para[1])))):
event = Event({
'assetid': self.graph.deviceid,
'meastime': df.index[0],
'level': 1,
'info': '电子定子温度异常！'
})
self.graph.events.append(event)
# 三相温度大于* => 定子温度异常
if np.any((df['A'] > para[5]) | (df['B'] > para[5])
| (df['C'] > para[5])):
event = Event({
'assetid': self.graph.deviceid,
'meastime': df.index[0],
'level': 1,
'info': '电机定子温度异常！'
})
self.graph.events.append(event)
# 轴承绝对温度大于* => 轴承温度异常
if np.any((df['temp_bearing_front'] > para[6])
| (df['temp_bearing_rear'] > para[6])):
event = Event({
'assetid': self.graph.deviceid,
'meastime': df.index[0],
'level': 1,
'info': '电机轴承温度异常！'
})
self.graph.events.append(event)
def test_initializer(self):
node = Node(Event(3, title=u"title", summary=u"summary"))
self.assertEquals(node.title, u"title")
self.assertEquals(node._id, 3)
self.assertEquals(node.summary, u"summary")
self.assertTrue(node.speaker is None)
self.assertRaises(AttributeError, Event, 3, t=u"title")
def get_alarm_logical(self, p4, p5):
"""
0-1型信号
"""
df = self.graph.get_data_from_api(['signal1', 'signal2'])
if not df.empty:
n = np.sum(np.abs(df['signal1'] - df['signal2']))
t = n * df.dt
if t > p4 or n > p5:
event = Event({
'assetid': self.graph.deviceid,
'meastime': df.index[0],
'level': 1,
'info': '双仪表数值不匹配'
})
self.graph.events.append(event)
def get_alarm_analogs(self, p2, p3):
"""
模拟量信号
"""
df = self.graph.get_data_from_api(['signal1', 'signal2'])
if not df.empty:
df = df.resample(f'{p2}S').mean()
r = (np.abs(df['signal1'] - df['signal2']) /
np.mean(df['signal1'] + df['signal2'])) * 100 # 双仪表相对偏差（%）
if np.any(r) > p3:
event = Event({
'assetid': self.graph.deviceid,
'meastime': df.index[0],
'level': 1,
'info': '双仪表数值不匹配'
})
self.graph.events.append(event)
def get_alarm(self):
df = self.graph.get_data_from_api(['sv_ref', 'sv_act'])
if df.empty:
return
algparas = self.graph.parameter
rolling = df['sv_ref'].rolling(algparas[0] * df.dt)
roll_cv = np.abs(rolling.std() / rolling.mean()) # 计算窗口变异系数
min_cv = roll_cv.max()
if min_cv > algparas[1]: # 数据包不包含稳态工况
return
stidx = roll_cv.argmin() - np.floor(algparas[0] * df.dt / 2)
edidx = roll_cv.argmin() + np.ceil(algparas[0] * df.dt / 2)
n = (edidx - stidx) // 5 # 对稳态段切头切尾
avg_ref = np.mean(df['sv_ref'][stidx + n:edidx - n])
avg_act = np.mean(df['sv_act'][stidx + n:edidx - n])
r = np.abs(avg_ref - avg_act) / avg_ref * 100 # 计算稳态工况设定值与实际值偏差
index = Index({
'assetid': self.graph.deviceid,
'meastime1st': df.index[0],
'feid1st': "16000",
'value1st': r,
'indices2nd': []
})
self.graph.indices.append(index)
if r > algparas[2]:
event = Event({
'assetid': self.graph.deviceid,
'meastime': df.index[0],
'level': 1,
'info': '伺服阀零偏报警'
})
self.graph.events.append(event)
def setUp(self):
self.node = Node(Event(3))
self.attrs = self.node._wraps._attrs.iterkeys()
def test_add_conflicts(self):
e = Node(Event(5))
self.node.add_conflict_with_node(e)
self.assertTrue(e.get_id() in self.node.conflicts_with)